yeah it is with cameras you look quite funny at the moment tim this is not supposed to look like just ah sod that okay there nice group k da yeah yeah i guess whatever it is yeah i never get this so what is the point of if this is strapless if this strap is supposed to go behind m i obviously doing this wrong obviously failing this like whatev calling center and tra you you have this going behind your ears the yeah like this and then yeah you know what the shit has to do now yeah look is good yeah okay yes so actually i just realize i do not have power let me just switch on the other which is going to run out okay okay so there is probably not much to talk about at the moment in terms of like talking about each other is stuff i mean beyond what we have been talking about yesterday yeah that is true i think so what i have been thinking is maybe if we try to really make sense together of the x m l format to be sure that we can all produce the data that we are producing in a way or at least i mean i guess my data i will probably load in from completely different way anyway because it is matrix but all this stuff that goes with annotation that we have it in the right format maybe if we just sort of pool what we know about the x m l format and try to make sense of it i do not at all know how to go about this what i did just now is i downloaded i downloaded some files some x m l files and i was thinking if we maybe just should go through some of them and like try to see if you understand the general structure and pool together what we think about the structure 0 yeah what we think that things mean and then talk about how the annotation for information density and stuff should maybe be structured in a similar way yeah or but i do not r i do not really have an idea like what other words what we would do today in this meeting i mean yeah we have not got that much to talk about i do not think no just maybe talk about how you would give me your data file yeah yeah yes we can do that what is that part that is the interface between having the topic segments and calculating the information importance of words yeah so i i would need separate files for each segment or just maybe have delimiters inbetween each segment alright yeah well that that is the way it works it has got delimiters in between the mar the boundaries okay yeah and y then i can make files of that but yeah if you are happy to do that yeah alright and you are saying you need a a label for each segment but i mean the most i ca you do not need a one no i do not kno know it no well i could give you an i d but it would be quite difficult to actually give a topic a title yeah that is fine i just i just need a a title for the file but does not matter what it is right okay yeah that is no problem yes so does anyone have a good idea where to start like which what so what is it we talk about getting in at the moment we are basically just cause the 2 of you will merge your data together right in some way like i in the end it will result in one annotation yes so the only thing we are trying to tie in is just an additional annotation which is similar to the annotation about what what would it be s most similar to simply to the segmentation information or yes perhaps yeah but i mean they are all very very similar all those x m l files okay i had a look at the for example a s i am not sure it ah see that is what i was thinking not sure what software i have at the moment which would be hello yeah ah does anyone know any good standards software displays x m l i am not sure take mozilla yeah but i am afraid i only have firefox and firefox does not have the nice x m l viewer anymore i think i do not have mozilla no i can probably just emacs them so we have then when we are mixing our values together is it a value for each expression for each sentence or something yeah i think that would be m i mean for me it is quite difficult to say 0 on yeah you know w what scope one annotation would have i think it would be certainly more than one word and i think it would work best with utterances or segments yeah yeah i would just end up with values for each word so i would not have yep any boundaries for segments at all but i just i just would have the words and then how we how long each expression would be i do not know but going from from a word business would not it be easier to then go back and calculate for each utterance yeah but what is an utterance yeah you know wha wha each turn so if we i think just the segments as they are segmented in the for example in the point se 65 okay yeah so what we what we would fit in into the xml files would it be a value for each utterance or what you i think so okay and you would s probably still need your values for the words perhaps for for the keywords that are displayed when you click on something so then we would have to fit in more than just one where values yeah but that is another representation then i think f for the importance measure so d yeah does anyone does anyone know in which file the actual like what is it called the splitting into the utterances but should yeah po is it not segments is is called is that yes or is it dialog act dialog acts no it is not it is for example there is b d b 001 c 6 is it what does it end with does it with with sax does that look like it i can not see anything very much but yeah that is that is it yeah huh okay i think that is it yeah be nice yeah they have all got an i d for each utterance let us actually just see if maybe k right 0 this is how much time you spend just getting the right software going is that better to see i guess yeah white and black is better to see yeah it is better can you use a bigger font so just see if i can view okay so okay so here there is for each segment that is really all segments i mean it is it can be words or topics or anything i think this this that is why th it is for example time provenance dialog act or time provenance segment or time provenance yeah and it has got a different file for each speaker but what is this i mean this does not contain any content like any text no but it yeah n no it just points to the to the words 0 wait somebody s s somebody explain that slowly to me so yeah it p 0 well some of them point to the words other others point to the dialog acts yeah yeah so that is kind of the global thing that ties together other things so what do all these things in this file have in common what what are they yeah they have their nite i d the time no but w like so they are they are segments in what sense wh what is definition of a segment then the i suppose so the utterances but also includes things like like the segment is what is displayed in one line by nite okay and and that is usually dialog what what is difference i between provenance dialog act and between segmenter i do not yeah it looks the segment thing looks in the into the words x m l file and the dialog acts thing yeah i think it is in a different a dialog act a dialog act annotations of dialog act so is that just another representation of text no their their annotation of dialog acts and one segment can have or one of those word strings that are presented in one line can have several dialog acts annotated on it that is why just see can we have this files somewhere so the stuff that is that is called segment here that do we know which file this is pulling from from yeah it is it is c it from words x m l is that it is saying here yeah it is from words yes let me see if i have that words x m l file so let us for example look at that is just below so we n would need a value label for each of those segments that are not dialog acts for all those that point to the actual words okay let me just try to get them all to a different screen is that what we were sorry i am just i am trying to arrange them so that we have one above the other so to make some sense of that so no here we had but i think these segments are perhaps not exactly what we are looking at because that is just 01 tying all the others together and the information or r i mean we are going to create a file that looks more like the words file i think i am just trying to understand what this segments file is about like it is it is referencing here it is referencing to to a certain position in the in the words file yeah think so so it is that non vocal sound one here that is the mic noise okay but what is the does it always just 0 no it is it is referencing to several word structures so this would be basically be a a sequence right yes so that is from w 52 are they not ordered or 0 yeah from w 52 down to w what are we talking about yeah they are and yeah w sorry to discourse marker dis f marker fi 5 yeah that is there no no up up yeah that is there no above go up yeah right yeah okay so this so this is somebody saying it it does not do we have any confirmation do we actually see from this file who is doing it just out of interest yeah yeah this is all the speaker d you and the speaker d 0 sorry in speaker d is file so this is where he would like time stops and then it only starts at a later time because 0 you do not see when i am pointing at screen it is just for this so then it starts here this time against speaker d because in between there is somebody else yes you can see whether there is somebody else or not okay you can see that in the w w 1st it is w 554 and then it is 76 so there are this number of utterances of other speakers in between yeah okay okay so the the file with annotations for information density what would that be like would that be like the segments file giving a start word and end word or or would that how would that be to best tie in with the system yeah i think we would also need 2 of those sorry yeah i think we would have to have the same structure one that points to it where we tie together all our information so we would perhaps have to make one that is similar something like something like this or like where you give sort of a reference to a beginning and an end position yes and then another one that would perhaps give the actual probability value well that would be in the same file would not it it would just could not we have a like yeah it could could probably be in the same file i do not know i guess we could introduce a different but if you have several layers then you can not represent them on the same file we have for now if we just do something which for every segment in here attaches actually can we not tie it to the segments file would that not be the way how they would want it to have to do as if we in the end if we want to attach to each of those segments one number for now and those segments have do they have an i d yeah i guess they do so this is the i d of a specific segment right yeah in segment one there line below yeah so would not it i mean the problem is they have me have not looked at the exact inner workings of their of their engineered but you would think that the easiest way and the way that how it is intended to be would be just if we have here a link to the segment a like an i d for that segment that we just create another file which links to the segment and then has an additional value which is the number so you mean an a an attribute yeah yeah yeah yeah that is what i meant actually yeah yeah 0 sorry i i thought we were talking about having 2 files i was think yeah i i think that i i understood wrong i thought you were wanting to have 2 different x m l files with one the reference and one just th just the number and i was thinking that is probably what you meant just having like for each sort of of our segments having just the i d which is referencing to these segments here and another attribute which is the the value so this whole information we would then store in this ah no that i do not have access to because i did not download like there is this one meta information file where it describes the structure of all the files and describes which which attributes they bring in so we would add that to that file saying that sort of we bring in information density and then we would create the file of that type which we probably could not call it segment i am not sure we probably might have to have a different word for it i am not sure if it there is any trouble with it repeating would not it be easiest to just incorporate a new attribute in in this file in the existing segments file yeah i do not know that is probably not very nice but would quite easy would not it yeah i reckon actually if we make a copy of it but i i mean there is not all the information that is in in the c corpus in there in the segments file yeah i think that is just the things that are loaded every time but we have lazy loading so it can load more than that perhaps we could just try to cope you know to make our file one of those lazy loaded but i mean i have no real idea how that works what what are you saying about the w i th does not the lazy loading apply to everything i mean that it sort of dynamically loads yeah but yes but it has you know a basic thing that it loads i think so for example every time it loads the segment things it can not it can not not display words i think you think it i it l it loads the whole of the segments file every time and it eras i think so that is how i understood it okay the segment at the moment is split up over because otherwise in the in the other things there is no information about what the participant name is and so on so in the segments file it is really the very basic that have to be displayed for any thing but they display the segments in or that the utterances i in their user interface as well where the words are y displayed so so they have they have their utterances displayed in their interface so that is they access probably this file and then they display the words so why should not we be able to do that no but i think it is too early to really like discuss that in detail because we do not at all understand at the moment how the internal data structure like how the loading works so maybe if we g if we go ahead do you think it would be possible for you to do an like something like segments or maybe just a copy of segment which has an attribute for for each segment but it is like with a with a value with a density value yeah i thin i mean it would not be difficult to create a file of the p sh of that shape i mean that would be no problem would it be easier though because all your methods are sort of not working with their whole time frame structure there so would it be easy for you to to tie the things together like if you are doing it on on the word basis here with those words that in the end you then tie it back in into the right segment here i mean you probably have to do a bit of yeah i i do not know how about you with your words but for my segments were the f 0 measurements and all those values i get from there i always store the beg start and end time of everything i calculated there so i would just have to pu put it yes you are doing it time based at the moment so this is not directly having time references but you can get the 0 it is actually here yeah it is okay so if you slice it up by time you would probably be able to just like attach like just some attribute of info val just f to each of those segments actu but actually my segments are not always the same as their segments because and their segments there are pause pauses sometimes yeah can you not just leaves those lines blank a yes but yeah i mean one segment of theirs is sometimes 2 segments of mine that is just what i meant 0 alright okay kay i do not understand enough of what their data structure is this probably would be a lot easier would i really understand how they are handling the data internally cause then we c then i could say 0 it is easy to just tie it in if you just have it time stamped that just reference by words and stuff but t at the moment so you would you are doing i you are doing a word by word base yeah the problem is probably that i extract all the words and then i do not use an i d or something for it so it would be difficult to write it back to the right position okay but every word does does the word picking like does it always have the same information value in your thing i does it depend on its position like it depends on the position i think it be d depends on the position yeah and you are doing this via some software that is like external software so you can not yeah i i could use another a different approach though so what if you if you if you get the results from that software and you go back over it then with that file sort of it you write an algorithm which which then goes back because they they are in the right order still and stuff right so that should not be a problem yeah 0 but this is by speaker here which makes it slightly more difficult the problem is that actually nite x m l probably provides a lot of the tools that we would need to do that yeah but for example i made a file that contains just the start times it could contain end times and words for all speakers of all meetings i mean that is just the same thing as i gave yeah as i gave to you in in a temporal sequence but displaying also a start and end times of every word so you could perhaps use it to match it with your additional information you got could not you yeah what did you use to make that file perl a perl script yeah just in perl you did not use an x m l parser no no okay would you think it would make sense to just take file files by speaker it is it does not matter what input i give to to rainbow so i just could g use the files as they are i do not know if that is that would give an output but but would the information density algorithm still make any sense if you split them up i do not know because i mean the whole i thought the whole thing is that you look at the frequency of a word in that specific how often a word inc occurs in a certain topic versus how often it occurs over the whole corpus and that from that it calculates yeah no it i think it is not versus the whole corpus it is you have certain categories and you measure which words have the highest information for one category so it is the categories across each other i think but would you then not get the typical words for every speaker if you probably yeah but yeah it is it is s strange yeah i have a gut feeling it is not a good idea to split it because it would then say how it find that it is that speaker yeah no but i mean it can not be that difficult if you if you already have you have in the right order all the words with a with a score to them yeah and you have a file which has each word and a time stamp yeah so those 2 tied together have each word and its time and it is probability yeah and and and its proba and its value or or value yeah yeah yeah but what what the problem is i do not know exactly i think the information gain in rainbow is ordered by the value of the information gain not i am not sure if i can get the right order and the values that is the problem so they are they are at the moment so but i if the order stays the same it is no problem at all to i just write back again but if it is ordered by information gain i do not know where the words come from because it is it has a bag of words representation it appears to me that rainbow was made for something quite different yes is it is it was made for text classification so could you put the more information back in then yeah that is the problem i actually like i are you actually sure that rainbow is doing a measure like is returning a measure of what we are trying to measure cause it it seems to me that it is just it sounds like something quite different in in d many aspects yeah what it actually does is that you you put in some documents and you have several documents per category and you have several categories and w and then it measures which words are typical for a certain t category yeah and if you get a new document it will compare which words are in that new document yeah and if there are a lot of words that are typical for one category it will assign it to that category and if it is typical for another one it will assign it to that one but where do you have the original category information from yeah it is because you have y you have oth different files if if this is your directory you have a diagra a directory 12 and 3 yeah yeah and this represents the category everything that is in there is in category one so where do you where do you have them from at the moment the split up a yeah but the topic is not it yeah just split it up somehow yeah that is a topic information in i in the t in the topics in the in the human topic s so you have split them up by topic at the moment yeah no i have just split them up somehow by there are several documents for each meeting and i just put one in each category yeah but so it is it is not very sensible at the moment because i am waiting for the topic segments or i am just yeah okay okay okay cause i was just thinking that does not make sense at all but yeah if you if that is just while you are waiting okay have you ever like looked into different ways of calculating cause i was just thinking like i mean the for example the the infor what is it called the entropy calculation is that she boxed it under the simple calculation that you could probably write the script in no time at all and yeah yeah probably yeah maybe it is it is better if i write it myself because otherwise it is too easy to just split things up into bins and it it would not be any work at all in terms of programming or something yeah but i am also just like i think that probably the entropy value at the moment for a word is closer to what we are at the moment looking for i can just like k k i can sit together with you for 20 minutes and just show you the entropy code that i wrote for my other project and it probably and we should work together because it is we used t we have to we would use the same matrix as i am using in my latent semantic analysis alright you would use to calculate entropy scores and then we would have a score which actually which would be the same for the word in each position so in that sense it is doing something a bit different and like w basically the score that i am talking about is a conditional entropy score which just checks how much information the fact that there is one word tells you about what would be the next word but that is a relatively good measure of whether that is a very specific word in which case they are usually words which tell you quite a lot or a very general word which usually does not tell you quite a lot yeah how does it calculate that actually it it is basically the it is the standard entropy formula okay and you sort of you you so how sure or unsure you are about the what is following our b our context measures yeah yes yes it has i mean i think the official le sort of the official description of what it tells you is how much that like the fact that a given word occurs tells you about what is the next word is going to be yeah which does not sound too exciting but it it just works out in the way that words which are promiscuous and which occur with everything all over the place have very low a scores on that and also usually end up being the words which are p least like expressive and l contain less information well like function words and stuff yeah function words or just very general nouns pr probably like what whatev for example the word computer in that context you could imagine it to f like be in all in all the contexts but do we have enough informa enough data for that it gives us sensible things because i mean the the words we are look really looking for appear not too often and if they appear 5 times in the meeting and they have each time a different you know differ some different surrounded words perhaps we have not enough data yeah so we would we w would not do it by word we would sorry okay i was i was getting that w actually sorry i was getting that wrong i was getting it from what i did my project now in this case we would do it by per mee words per meeting so yeah but that is the sa almost i think similar to what i am doing because words that are in every class i do not understand what you that are not very informative but words that are only in one class are are very informative for that class yes it it is probably doing it is probably doing quite the same thing in the end but i am just saying like with that thing you would easily have an algorithm which at the moment provides you for each word with a score which we can use 0 okay alright no i was i was i was describing the wrong thing in this case we would not be doing how much it tells you about another word in this case we would be doing given that you know a word how good is it at predicting from which specific topic that was yeah okay so that would yeah in that sense it is the same thing here yeah that is th yeah but that would give the same value for one word every time i mean for for a specific word yeah yeah for a specific yeah because then it would be quite easy to re integrate it into such a for a specific word per yeah the same word the word yesterday would be would have the same score all over the place per all over the place and in my case it would have i think it would have different values for each category okay but in that category it would have the same value at each place do you know what i mean i c okay but then it would also be possible to re integrate it it would be more dif i mean it would be a bit more work but then but but your category thing depends on that we not just have topic segments but also that these topic segments we have them in categories yeah you you do would not you need several documents for each category or several segments for each category that would be best but i i would have to look if that do but will it word without that at all at least it works if there are several categories with each with one document each but it yeah cause yeah i mean s so in our case basically every every s topic would be its own category and the question is does the algorithm still make any sense in that i do not understand the algorithm enough for that yeah that is the questio yeah i do not know but what i am really like because the entropical calculation is so simple maybe we should look into making that score just as a preliminary score that we have okay like it it is a very it gives you like i have looked at the result it gives you basically something in the end which vaguely tells you just whether a word is a very specific word or a very general word yes and i like there is some hope that probably having just sentences where there is lots of very specific words if you mark them as being more interesting than the words which are only very general words that they would get us somewhere and what well what score would a word get that just occurs once in all the corpus for example probably one0 is very high information value even though there are all the other same topics where it does not occur yeah cause this would yeah in in a sense i mean this is a bit like the what like document frequency over total frequency measure it sort of just going by so would it be higher scored than a word that occurs in every sequence of the same topic what do you mean every sequence of the same yeah i mean we have s our several topics for example we have 20 topics and for one of these topics there are 5 occurrences and we have one word that within th within the topic so like topic we had to when you say topic you mean like just like from from a beginning to end point like within one meeting there are several topics yes but across meetings there will be the same topic several times but we do not have that information anywhere do we ah it wasn okay i thought that was what you were doing well when it splits the topics up it does do it on regular words that that occur yeah but but it does not tell you what they are no but but you you are segmenting yeah but you make also segment similarity i think that was i i thought that would well i am i am doing one on segment similarity in the end yeah yeah i am doing like finding similar segments basic latent semantic analysis but but like f for now like your segmentation is just splitting a meeting up into different blocks ver yeah yeah you can not really get any other output and they they are not related then to other blocks of not not from what colin is doing from what i no no okay no it is only like i am writing an algorithm that which then tries to also again based on word p occurrence patterns try to link together maybe different ones of those i 0 okay i misunderstood that then so so yeah how would it work i mean what would the his information then be useful for in r rainbow i thought the point about that was that we would put into s serial categories all the segments of the same topic across meetings so that yeah 0 no i li i th i thought something else that we d we just split i need somewhere to split okay and that splitting at category boundaries splitting at topic boundaries would a nice thing to do rather than just splitting somewhere yeah it is supposed to split it into cu coherent topics with the similar information so yeah yeah yeah but but then it does okay yeah i am also a bit b like i am not a 100% sure about rainbow being the right thing cause it seems that rainbow does in its structure quite rely on having different examples of the same category sort of in in a way yeah but it i think it should work for just one document because it it compares between the categories and if you just have one document it still can find out which words are informative for that category and which are not so if you have just one document in each category and there are a lot of occurrences of the word the in each one kay so this word will be not very informative across the categories so it should work for one but i am not sure if how exactly it it calculates everything i think it is not possible to look that up you know what as a byproduct of my l s a i will provide a vocabulary like sort of a dictionary which for each word gives an entropy score entropy score which just tells you of how much information the presence of a word tells you about which topic it is not which category like i am not i am not lumping together separate topic segments into categories but just like how much this word tells you about wh how likely that w the occurrence of that word makes it that it is a specific segment topic segment which is some measure already of how widespread this word is versus how specific f to a certain segment that is and i will just provide that because that is just not much more work than just the usual thing and then we can see how we can tie that in with the other stuff so if you keep on working on rainbow meanwhile and try to find a way how to tie your rainbow stuff into yep some way that we can attach it to a certain time segment yeah maybe it is possible to have a list that it is 0 that is ordered by yeah do not know i am just thinking wh if it it used is each word completely unique like sort of does it treat each word each occurrence of word as a completely unique event or does it i mean no it it has to i mean basically the the form of the word is important right we can not just replace the word by an arbitrary string because it looks if the same word occurs again and stuff i do not know what you mean by that yeah it it has to work with a m yeah well i think it is a stupid question it is like it it it has to work on on on the word like on what i was thinking is whether if we replace the word by something uniquely id identifiable then it would not make a different which order it is 0 right but that would not work because it needs the word because that is all it is working on it is the word and that looks if that word occurs again and versus how often that word occurs in other context right so we can not attach some type of information to the word just to the word string itself yeah like making an underscore making the time or something that would not work yeah alright yeah you you can use some kind of truncation maybe if you attach a number to each word and say that it should omit the last part but would it have that in the untruncated version then like would it s would the output be the untruncated version probably not it will probably no i do not think it would no no yeah maybe i should try something different and just program it for myself because yeah for now i mean really just like yeah i think if we work together on an on an entropy based score it is let me see if i can demonstrate t i mean let us just keep on talking meanwhile and i will try to start that up it is it is it is really d it it is a very simple thing but it it basically just does something which tells you how specific a word is in a sense it is in a it is it is basically just to to a high degree really telling you how r how rare a word is or how common a word is but as as the 1st step that is probably for for a prototype for next week that is probably not a bad thing i mean even if it is just that even if you just like if you have segments where where lots of rare words occur highlighted in darker red than segments where all the very common words occur yeah that is just that is somewhere to start from and it is it is a bit more sophisticated than that but then de facto it just ends up doing that mostly from what i figured out actually i think i am not going to not going to start that now because that is probably going to take too long so if we get it on a word by word basis whatever you do it will probably appear in a word by word basis and what you have on a sort of segment but not quite segment base yes yeah i mean i have not really decided on how to really get the information out of this now because when i have for example increased speaker overlap that applies to several turns of course but i could give the information about yeah how how important this is to each of those segments kay what about the following model i mean this is a very unscientific way of doing it in some sense but what if we if we take time as the standard unit for now and sort of like make a massive one segment split super array because everything you are doing can in one way or the other be ti tied down to actual time so if we if we if for if for each one segment time slot we could attach a value and then it would be easy to then go back when if we have the time marks here and re map that onto onto the length of a segment you know what i mean so if you have for each word say and we know that word starts at this segment and ends at that segment and you have for like for some time period w the overlap and that period on the f ones and that period or something so you also have a value which can be tied down to a time and then we could just in m in matlab or in something just create some massive super array of of things for each for each like sort of time sampling slot and and calculate a value for this and once we have this array we can go with the script and sort of go for each segment to the starting and end times and say okay this is from our time segment f here to this time segment there so we take the some of those and divide them by by the number or something create the average and put it in as the value for the segment you know what i mean yeah but how can we get to know what makes sense as a a function for joining everything together 0 might be difficult to find that out tha that is that is a fiddling play in the end but that problem we will always have yeah because we do not have a useful way of automatically evaluating we do not have a way of usefully evaluating automatically what is good and what is bad so it is it is probably always going to be a question of looking at it and saying okay like running it with different different factor loadings and seeing okay this way it works this well and this way it works that well yeah pros but you proposed we we should break that down to have the smallest smallest unity of time duration should be one word i mean that would be because that would be what naturally came out of her thing but it probably be more difficult for mapped t to map for you to map it on so for you sort of i and yeah sure but i could then if i if i have the value for a segment i would perhaps just give all the important wor or yeah no you you could always find out how many words there are in an utterance could not you yes yeah so yeah but so you say that inst like you basically say w having an array where each each cell is is one like is 0 is one word and then you would map your information onto individual words yeah i would say that someone have to break it down aye i would probably somehow have to break it down to to that level yeah would you be able to find out which word that is and yes sure kay and and then we could have like some type of just point in the end where the one scores from the all the individual word cells get multiplied all with like for each utterance or whatever you have get all multiplied with the same value all the ones that are within that utterance that is sort of the combination of of the 2 scores yeah probably somehow it should work that way for example and then we would have to go back again and then put that back into that segment mode here so that we because in the end we do not want it on a per word basis but probably on a per segment base yeah but then okay but th at that moment it would be better for me to just make it on a per segment basis right way so but and let her adapt it also to the segment basis 0 yeah yeah actually that yeah that is true so that it is easier if you are able to yeah well i think that is probably back where we started at this but you said it wi but you said it is more complicated because your segments are not those segments exactly yeah yeah i mean mine are only more but they have the same start and end point sometimes they are 2 segments and one with a gap in between but the have th th they do not overlap and their segments do overlap no no they do not but the tho those do or those do not they are i mean my segments overlap in the same way as theirs do but sometimes i have split one of the segments into 2 segments but it is easy to match the start and end times to map it to their segments okay so you could on their granularity you could on their granularity create a score like for each for each of their segments i hope so yeah well i guess i mean for you if you know for each word if you find that out then it has to be possible yeah then we sh yeah because if we know sort of this is going from word to word or it this is going from time to time and then there has to be a way then for you say okay this concerns these following words and then just make make a simple mean over them i think an interesting thing is if we do not combine your 2 scores in the in the x m l file you had but if we do that in the software then we can probably make ways of playing with it in the software and sort of l you know like adapting some sort of control like playing around with look like it is playing with different weightings for that the utterance based one versus the word based one and sort of look at it dynamically you know what i mean like playing around sort of figuring out what is the best way of combining them by playing around and looking at the results yeah we could even yeah we could even have a look at our different measures if they that would be quite difficult but yeah come up with the same same kinds of yeah yeah yeah we could probably like make a graphic display initially at least for our experimenting we would just place them in different ways and then see how they interact with each other yeah i mean that is what i am already doing with my s separate okay yeah so do you think y like both of you then can map something onto their segments like just each of you provide one value like double value or whatever like one decimal value or whatever onto onto exactly their segments i think if i can provide something for the words as they are it is stated from where to where the segments go it should be should be possible it is stated both in yeah it is tells you even which words it is yeah so it must should be possible it is done both in terms of words and in terms of segments it is a bit sad sort of that we do this before we have truly figured out how the nite x m l thing works yeah because now we are doing it all by hand and like parsing and un parsing that thing and it is it is all part of the framework yeah how much easier would it be if we truly understood this 0 i am sure it would be quite straightforward some of the tasks we have to do so but yet no one really understands them the actual x m l parsing kay i might just change my order of in which i do things and like forget my latent semantic analysis stuff until the weekend and try to really make sense of the of the nite data system now so that maybe as soon as i have understood that we find ways of doing that in within the nite framework already so that we do not manually have to parse times and entire things together you mean by matching strings or what well at the moment what you would do like to to solve this problem is you would sort of like write some perl script or something that gets this time value out of here and yeah but what i have done is a parser an x m l parser where you can get the start times okay so yeah that is quite easy because it is it is an attribute and you just s say that you want the values of those attributes so provided that you get your words in the right order do you think it is an easy task for you to it it is a f relatively feasible task for you to to get just a single value per segment i think that should be possible i do not know how long it takes me but okay and you say you think you are able as well to map onto those segments yep should be possible and if you are both able to map into those segments then we should be able to get one file where we have like whatever 2 values value a and value b both as attributes for for this and that we could load into a prototype and see what types of disp what ways of displaying this information are there yep yeah i hope to have some value quite soon but i just worked i mean i just calculated the values values for the average f zeros and they are what is c i mean i did not have a look at the data yet but they vary quite widely even for the same speaker across meetings so one speaker had an average of about a hun 100 in one meeting and 160 in another meeting that is really that is strange i mean perhaps it is because of laughter or something that is what i was thinking but i did not have there is probably also social interaction factors in that there is sometimes just a meeting like if people adapt their f 0 to each other then they are sometimes p i will probably look at it yeah that is what i read in one of those papers as well but yeah yeah but it could mess up things quite considerably can you not do something like just like not measuring the f 0 or the amplitude at all but just like the variance of f 0 within a certain time frame and like sort of like just have some part for its very low variance with more the same f 0 and one where there is a lot of more variance i do not well actually i do not know about that at all yeah but ar yeah but all this is quite you know data intensive yeah i when i let the just calculate the average f 0 levels sorry it i think it needed more than half an hour considerably more than what have half an hour f to delay it for our meetings because yeah m i mean we have 75 hours per yeah av average of 6 speakers and they are measured every 0016 seconds and that gives us quite a lot of values okay so at the moment you you just you measuring the f zeros relative to the average for the speaker or yeah what i did at the moment is i yeah i got the f 0 values from for each speaker for his headphone and i only take those the w that were recorded at the time where he was actually speaking and for those i calculated the average 0 ri so the average so that gave me now one value per speaker per meeting okay so what you would be feeding in would be just like one value per speaker per meeting so that oka that that is that is your average baseline okay okay no no that is just the average no that i yeah no yeah that that makes m yeah that makes a lot more sense yeah so that would show you how much relative to how he sort of how he is performing generally in that meeting relative to that how he is in a specific segment he or she yes i mean i r just needed to have this value now to relate m how yeah yeah yeah and for you that would be quite easy to translate into those segments yeah guess i have asked this question 15 times now yeah i think yeah is laughter annotated at all sorry amusement and so on is yeah because you could take that on ours out maybe if you take the laughter out and then calculate it yeah i i i mean it i actually have to look at the data what causes these yeah because it is quite funny to have a m male speaking at 2 hun 200 0 they w they do exist yeah sure but the castrati corps of the international computer science institute 0 well i am afraid i have to go soon but not quite sure i mean is there anything more we have to talk about anyway not the moment maybe if we meet at the weekend 0 i do not know yeah see as soon as as soon as i am halfway through my l s a like basically as soon as i have the the matrix built if of the document by word stuff it is very easy to then calculate for each word a score yep and i can just give you those scores and you can do with them whatever you want so no how about the the prototype if we want to show him something on monday we definite have to work together some of us at least have to work together to get it running probably yeah i think colin dave and me will actually work on on the java stuff and it and we will just see whatever you whatever you supply us we will try to tie in and visualize in some way or another alright i will ask jonathan if we can postpone the meeting to 10 clock yeah that sounds like an idea so that would give us a chance of meeting in for an hour before that to discuss the questions that we had yeah yeah just send us an email and tell us what is happening yeah i have not gotten like sort of my confirmation that w wednesday is fine i am not sure if i am supposed to expect a confirmation for my confirmation from him now or but i will just email him again and s ask him if we can maybe make it one sorry we said 12 and i am asking if he can make one this is a bit frustrating at the moment this project is not it it is so like difficult to to get to the point where you understand enough to really feel that i do not know i am not feeling that i am really working at the moment i am more just trying to make sense of everything and it is a bit too far into the meeting for that and into the project for that yeah and i am at the moment fiddling around with my data and not quite seeing how i get to a sensible abstraction level you know from my very i guess as soon as we have a framework in the w in the type of the prototype where like sort of each of us can tie in their stuff and see what it l how how it looks like and how it performs you know that probably makes it a lot easier then but it is sort of it is a boot strapping problem like for the prototype we need some type of data but to develop the data it would be a lot easier to to have the prototype yeah anyway i got to go yeah so i mean you will be happy with some data even if it does not make much sense yeah me too yes but if it is if it is in a form which is easy to read in at the moment that would be fine sort of like if basically if we have something like this segments file but for each of you like just have one attribute i think it is really easy if we do not merge them before hand but if we let them if we combine them in the prototype or do not at the moment because then we can easily d display them individually c contrast them to each other and play around with how y to combine them try try yeah yeah and also we do not have to recalculate if just one exactly yeah yeah and i mean computationally multiplying 2 integers or doubles or whatever should not be the thing that slows us down yeah if it is already on segments base that is not too much yeah okay alright right
i am it is with cameras you look quite funny at the moment what this is supposed to look like i saw that nice okay there yeah yeah i guess yeah i guess whatever you see i never get this so what is the point of if this is if this is supposed to go behind i am obviously doing this wrong obviously failing this like whatever calling center you have things going behind your ears like this and then it is good yeah you know what this shit has to do now yeah yeah 0 okay yeah so let us just realize i do not have power let me just switch on the other way you are going to run out okay okay okay so it is probably not much to talk about at the moment in terms of like talking about each other is stuff i mean beyond what we have been talking about yesterday yeah that is true i think so what i have been thinking is maybe if we try to really make sense together of the xml format to be sure that we can all produce the data that we are producing in a way or at least i mean i guess my data will probably load in a completely different way anyway because it is a matrix but all the stuff that goes as annotation that we have it in the right format maybe if you just sort of pool what we know about the xml format and try to make sense of it i do not at all know how to go about this what i did just now just now is i downloaded some files some xml files and i was thinking if we maybe just should go through some of them and like try to see if you understand the general structure and pull together what we think about the structure what we think that things mean and then talk about how the annotation for information density and stuff should maybe be structured in a similar way yeah but i do not really have an idea like what otherwise what we would do today in this meeting i mean yeah we have not got that much to talk about it i do not think just maybe talk about how you would give you your data file yeah yes what is that what is that part that is the interface between having the topic segments and calculating the information and importance of words so i would need separate files for each segment or just maybe have delimiters in between these yeah well that is the way it works it has got delimiters in between the boundaries yeah and i can make files of that yeah if you are happy to do that yeah and you are saying you need a label for each segment no i do not you do not need the one i could give you an id but it would be quite difficult to actually give a topic title i just need a title from the file it does not matter what it is okay yeah that is no problem yes so does anyone have a good idea where to start like which what is so what is it we are talking about getting in at the moment we are basically just because the 2 of you will merge your data together right in some way like in the end it will result in one annotation so the only thing we are trying to tie in is just an additional annotation which is similar to the annotation about what would it be most similar to simply to the segmentation information yes perhaps yeah i mean they are all very very similar all those xml files i had a look at the for example ais not sure see that is what i was thinking i am not sure what software i have at the moment hello  yeah ah does anyone know any good standard software at this place i am not sure take no 0 yeah but i am afraid i only a firefox and firefox does not have a nice no view anymore i think i do not have myself i can probably just image then so what we have when we are mixing our values together is it a value for each expression for each sentence or something yeah i think that would be i mean for me it is quite difficult to say on you know what scope one annotation would have i think it would be certainly more than one word and i think would work best with utterances or segments yeah i would just end up with values for each word so i would not have any boundaries for segments at all i just would have the words and then how we how long each expression would be i do not know but going from a work business would not it be easy to then go back and calculate for each utterance yeah but what is in utterance each turn so if i think just the segments as they are segmented and it yeah so what we would fit in into the xmif files would be a value for each utterance or would you i think so you would probably still need your values for the words perhaps for the keywords that are displayed when you click on something so then we would have to fit in more than just one way of values yeah but that is another representation then i think for the importance measure does anyone know in which file the actual what is it called the splitting into the utterances is it not segments it is called dialog up no it is not doing it it is for example there is bdb 0 one what does it end with with sex does that look like it i can not see anything like that but yeah that is okay i think that is it yeah we have all got an id for each utterance that is actually just to see if maybe k write how much time you spend just getting the rest of stuff to go and is that better to see i guess yeah white it is better to see yeah it is better to figure it off one just see if i can okay okay so okay so here is for this segment that is really all segments i mean it is it can be words or topics or anything i think in these that is why it is for example time provenance dialog act or time provenance segment or time provenance yeah it has got a different file for you speaker but what is this i mean this does not contain any content like any text no it just points to the words 0 wait somebody somebody explained that slowly to me so yeah some of them point to the words others point to the dialog acts so that is kind of the global thing that ties together so what do all these things in this file have in common what what are they yeah they have their night id the time no but like so they are they are segments in what sense what is the definition of the segment then i suppose the utterances but also includes things like the segment is what is displayed in one line okay and that is usually dialog what is the difference between prominence dialog action between segment yeah it looks the segment thing looks into the words xml file and they use dialog acts i think it is cats in a different are dialog annotations of dialog acts is that just another representation of text no there are the annotation of dialog acts and one segment can have one of those word strings that are presented in one line can have several dialog acts annotated on it that is why just you can get these files and some of so the stuff that is called segment here do we know which file this is pulling from yeah it is from words xml is that what it is saying here let me see if i have that word is x tml file so let us for example look at so we would need a value label for each of those segments that are not dialog apps well all those that point to the actual words let me just try to get the multi difference screen i am trying to arrange them one above the other so to make some sense of that so now here we had but i think these segments was perhaps not exactly what we are looking at because that is just one tying all the others together and the information or i mean we are going to create a file that looks more like the world is file i am just trying to understand what this segment file is about it is referencing here it is referencing to a certain position in the words file yeah i think so so it is that non vocal sound one here that is the mic noise okay but what is does it always just no it is referencing to several words sometimes so this would be basically be a sequence right yes so that is from w 52 are they not ordered or yeah from w 52 down to w what are we talking about yeah to this course marker this f marker 5 yeah that is there no up up okay so this is somebody saying it does not with any confirmation do we actually see from this file who is doing it just of interest this is all speaker d 0 sorry in the speaker d is file so this is where he is like time stops and then it only starts it only starts at a later time because you do not see when i am pointing in screen interest for that so then it starts here at this time again speak a deep because in between there is somebody else yes you can see whether there is somebody else or not you can see that in the w 1st it is w5 54 and then it is 76 so there are this number of utterances of other speakers in between okay so the the file with annotations for information density what would that be like would that be like the segments file giving a start word and end word or would that how would that be to best tie in with the system yeah i think we would also need 2 of those so yeah i think we would have to have the same structure one that points to it where we tie together our information so we would perhaps have to something like one something like this or where you give sort of a reference to a beginning and an end position yes and then another one that would perhaps give the actual probability value that would be in the same file would not it it would just yeah could could we have a like i guess we could introduce a different but if you have several layers then can represent them all in the same file for now if we just do something which for every segment in here attaches actually can we not tie it to the segments file would that not be the way how they would want it to do us if we in the end we want to attach to each of those segments one number for now and those segments have do they have an id yeah i guess they do so this is the idea of a specific segment right in segment one the line below yeah so would not it i mean the problem is they have not looked at the exact you know workings of the engine yet but you would think that the easiest way the way how it is intended to be would be just if we have here a link to the segment like an id for that segment that we just create another file which links to the segment and then have an additional value which is the number so you mean an attribute yeah yeah yeah that is what you know yeah sorry i thought you were talking about having 2 files i was thinking yeah i think i understood wrong i thought you were wanting to have 2 different xml files we won the reference and won just the number and i was thinking that is probably what you meant just having like for each sort of our segments having just the id which is referencing to these segments here and another attribute which is the value so this whole information we would then store in this that i do not have access to because i did not download like this this one meta information file where it describes the structure of all the files and describes which attributes they bring in so we would add that to that file saying that sort of we bring in information density and then we would create a file of that type which we probably could not call it segment i am not sure we probably might have to have different word for it i am not sure if it is there is any trouble with it repeating would not it be easiest to just incorporate in your attribute in this file in the existing segments file yeah i do not know that is probably not very nice but it would be quite easy yeah i reckon actually if you make a copy of it but i mean there is not all the information that is in the corpus in the segments fire i think that is just the things that are things that are loaded every time but we have lazy loading so it can load more than that 1st we could just try to cook you know to make our file one of those lazy loaded but i mean i have no real idea how that works what are you saying about the does not the lazy loading apply to everything i mean that it is sort of dynamically loads yes but it has you know a basic thing that it loads i think so for example every time it loads the segment things it can not it can not not display words i think i think it loads the whole of the segments file every time i think so that is how i understood it the segment at the moment is split up over because otherwise in the other things there is no information about what the participant name is and so on so in the segments it is really very basic that have to be displayed for any thing but they display the segments in or that the utterances in their use interface as well where the words are displayed so so they have they have their utterances displayed in their interface face so that they access probably this file and then they display the words so why should not be able to do that you know what i think it is too early to really like discuss that in d d because we do not at all understand at the moment how the internal data structure like how the loading works so maybe if we go ahead do you think it would be possible for you to do an like something like segments or maybe just a copy of segments which has an attribute for each segment which is like with a with a value with a density value yeah i mean it would not be difficult to create a file of the right shape i mean that would be no would it be easier though because all your methods are sort of not working with their whole time frame structure there so would it be easy for you to tie the things together like if you are doing it on the word basis see with those words that in the end you then tie it back into the right segment here i mean you probably have to do a bit of i do not know how about you with your words but for my segments of the f 0 measurements and all those ways i get from there i always store the big start and time of everything i calculated there so i would just have to put it out yes so this is not directly having time references but you can get that 0 it is actually here okay so if you slice it up by time you would probably be able to just attach just some attribute of info well just to each of those segments but actually my segments are not always the same as their segments because in their segments they are pauses sometimes if you could not just use those ones blank yes but yeah i mean one segment of theirs is sometimes 2 segments of mine that is just what i meant okay okay i do not understand enough about the data structure it would be a lot easier would i really understand how they are handling the data internally then i could say 0 it is easy to just tie it in if you just have a timestamp or just reference by words and stuff but at the moment at the moment so you are doing a word by word base yeah the problem is probably that i extract all the words and then i do not use an idea or something for it so it would be difficult to write it back to the right position but every word that is the word picking like does it always have the same information value in your thing or does it depend on its position it depends on the position it depends on the position and you are doing this via some software that is like external software so you can not yeah i could use another a different approach so what if you if you get the results from that software and you go back over it then with that file sort of you write an algorithm which then goes back because they are in the right order still stuff right so that should not be a problem but this is by speaker here which makes it slightly more difficult the problem is that actually night x mail probably provides a lot of the tools that we need to do that i made a file that contains just the star times it could contain end times and words for all speakers of all meetings i mean that is just the same in a temporal sequence i gave yeah as i gave to you but displaying also start and end times of every word so you could perhaps use it to match it with your additional information unit yeah what did you use to make that file perf that is good yeah you did not use that xml password no no okay what do you think it would make sense to just take files by speaker it does not matter what input i give to rainbow to rainbow so i just could use the files as they are i do not know if that is but would the information density algorithms still make any sense if you split them up i do not know i thought the whole thing is that you look at the frequency of a word in that specific how often my word occurs in a certain topic versus how often it occurs over the whole corpus and that from that it calculates no i think it is not versus the whole corpus it is you have certain categories categories and you measure which words have the highest information for one category so it is the categories across each other but would you then not get the typical words for every speaker if you probably yeah but i mean it can not be that difficult if you if you already have you have in the right order all the words with a score to them you have a file which has each word and a time stamp so those 2 tied together have each word and its time and its value yeah yeah what the problem is i do not know exactly i think the information gain in rainbow is ordered by the value of the information gain i am not sure if i can get the right order and the values that is the problem so they are at the moment if the order stays the same it is no problem at all to just write it back again but if it is ordered by information gain i do not know where the words come from because it has a back of words representation it appears to me that rainbow was made for something quite different yes it is it is it was made for text classification so how could we put our information back in there yeah i actually like i am i actually sure that rainbow is doing a measure like is returning a measure of what we are trying to measure because it seems to me that it is just it sounds like something quite different in many aspects what it actually does is that you you put in some documents and you have several documents per category and you have several categories and then it measures which words are typically for a certain category and if you get a new document it will compare which words are in that new document and if there are a lot of words that are typical for one category it will assign it to that category and if it is typical for another one it will assign it to that one but where do you have the original category information from yeah it is because you have the you have different files if this is your directory you have a directory 12 and 3 and this represents the category everything that is in there is in category one so where do you have them from at the moment that split up yeah just split it up topic in the topic in the human topic so you have split it up by topic at the moment topic at the moment no i have just split them up somehow by there are several documents for each meeting and i just put one in each category so it is not very sensible at the moment because i am waiting for the topic segment so i am just okay okay okay i was just thinking that does not make sense at all but yeah if you that is just while you are waiting okay have you ever looked into different ways of calculating i was just thinking like i mean for example the entropy calculation is such a boxed on that simple calculation that you could probably write the script in no time at all yeah maybe it is better if i write it myself because otherwise it is too easy to just split things up into bins it would not be any work at all in terms of programming or something yeah but i was just like i think that probably the entropy value at the moment for a word is closer to what we are at the moment looking for i can just like i can sit together with you for 20 minutes and just show you the entropy code that i wrote for my other project and it is probably and we should work together because it is we used we have to we would use the same matrix as i am using in my latent semantic analysis you would use to calculate entropy scores and then we would have a score which actually which would be the same for the word in each position so in that sense it is doing something a bit different and like basically the score that i am talking about is a conditional entropy score which just checks how much information the fact that there is one word tells you about what would be the next word but that is a relatively good measure of whether that is a very specific word in which case they are usually words which tell you quite a lot or a very general word which usually does not tell you quite a lot how does it calculate that actually it is basically the standard entropy formula so how sure or unsure you are about your context yes yes i mean i think the official description of what it tells you is how much that like the fact that a given word occurs tells you about what is the next word is going to be which does not sound too exciting but it just works out in the way that words which are promiscuous and the word which occur with everything all over the place have very low scores on that and also usually end up being the words which are least expressive and contain less information well like function words and stuff yeah function words are just very general nouns probably like whatever for example the word computer in that context you could imagine it to like be in all the context but do we have enough data for that it gives us sensible things because i mean the words we are really looking for appear not too often and if they appear 5 times in the meeting and they have each time a different you know yeah so we would not do it by word you would data sorry okay i am i was i was getting that actually sorry i was getting that wrong i was getting it from what i did my project now in this case we would do it by per meet words per meeting so yeah but it is almost i think similar to what i am doing because words that are in every class that are not very informative but words that are only in one class are very informative for that class yes it is probably doing quite the same thing in the end but i am just saying like with that thing you would easily have an algorithm which at the moment provides you for each word with a score which we can use now i was describing the wrong thing in this case we would not be doing how much it tells you about another word in this case we would be doing given that you know a word given that you know a word how good is it predicting from which specific topic that was yeah okay so that would yeah in a sense it is the same thing yeah that would give the same value for one word every time i mean for a specific word yeah yeah for specific yeah because then it would be quite easy to reintegrate it into a specific word per the same word the word yesterday would be would have the same word same score all over the place all over the place and in my case it would have different values for each category but in that category it would have the same value at each place okay but then it would also be possible for integrated it would be more different i mean it would be a bit more work but but your category thing depends on that we depends on that we not just have topic segments but also that these topic segments we have them in categories would not you need several documents for each category or several segments for each category that would be best but i have to look at that at all at least it works if there are several categories with each with one document each yeah i mean so in our case basically every topic would be its own category the question is does the algorithm still make any sense i do not know i do not understand the algorithm enough for that but what i am really like because the entropy calculation is so simple maybe we should look into making that score just as a preliminary score that we have like it is a very it gives you like i have looked at the result it gives you basically something in the end which vaguely tells you just whether a word is a very specific word or a very general word and i like there is some hope that probably having just sentences where there is lots of very specific words if you mark them as being more interesting than the words which are only very general words that they would get us somewhere and what score would the word get that just across once and all the corpus for example probably one is very high information value even though there are all the other same topics where it does not occur yeah because this would yeah in a sense i mean this is a bit like the like document frequency over total frequency measure it is sort of just going by so would it be higher scored than a word that occurs in every sequence of the same topic what do you mean every sequence of the same yeah i mean we have several topics for example we have 20 topics and for one of these topics there are 5 occurrences within the topic so like topic when you say topic you mean like just like from a beginning to an end point like within one meeting there are several topics yes but across meetings there will be the same topic several times but we do not have that information anywhere doing okay i thought that was what you were doing well when it splits the topics up it does do it on regular words that it does not tell you what they are no but you make also segment similarity i think that was i am doing one on segment similarity in the end yeah i am doing like finding similar segments based on latent semantic analysis but like for now like your segmentation is just splitting a meeting up into different blocks you can not do any other ip not from what column is doing from what i no it is only that i am writing an algorithm which then tries to also again based on word or current patterns try to link together maybe different ones of those 0 okay i misunderstood that then so so yeah how would it work i mean what would his information then be useful for in wainball i thought the point about that was that we would put into several categories all the segments of the same topic i thought something else that we just split i need somewhere to split and that splitting at category boundaries splitting at topic boundaries would a nice thing to do rather than just splitting somewhere yeah it is supposed to split it and to keep the topics with similar information yeah i am also a bit like i am not 100% sure about rainbow being the right thing because it seems that rainbow does in its structure quite rely on having different examples of the same category sort of in a way yeah but i think it should work for just one document because it compares between the categories and if you just have one document it still can find out which words are informative for that category which are not so if you have just one document in each category and there are a lot of occurrences of the weather in each one so this will be not very informative across the categories so it should work for one but i am not sure how exactly it calculates everything and i think it is not possible to look that up you know what as a byproduct of my lsa i will provide a vocabulary like sort of a dictionary which for each word gives an entropy score entropy score which just tells you of how much information the presence of a word tells you about which topic it is not which category like i am not lumping together separate topic segments into categories but just like how much this word tells you about how likely that the occurrence of that word makes it that it is a specific segment topic segment which is some measure already of how widespread this word is versus how specific to a certain segment that is and i will just provide that because that is just not much more work than just the usual thing and then we can see how we can tie that in with the other stuff so if you keep on working on rainbow meanwhile and try to find a way how to tie your rainbow stuff into some way that we can attach it to a certain time segment maybe it is possible to have a list that is ordered by just thinking is each word completely unique like sort of does it treat each word each occurrence of a completely unique event or does it i mean no it has to i mean basically the form of the word is important right we can not just replace the word by an arbitrary string because it looks if the same word occurs again and stuff yeah it has to work with i do not know what you mean by that yeah i think it is a stupid question like it has to work on the word like on what i was thinking is whether if we replace the word by something uniquely identifiable then it would make difference which order it is but that would not work because it needs the word because that is all it is working on it is the word and it looks if that word occurs again and versus how often that word occurs in other contexts right so we can not attach some type of information to the word just to the word string itself like making an underscore making the time or something that would not work you can use some kind of truncation maybe if you attach a number to each word and say that it is should omit the last part but would it have that in the untrancated version then like would the output be the untrancated version probably not no i do not think it would yeah i do not think it would yeah maybe i should try something different and just program it for myself because yeah for now i mean really just really just like yeah i think if we work together on an entropy based score it is let me see if i can demonstrate i mean let us just keep on talking meanwhile and i will try to start it up it is it is really it is a very simple thing but it basically just does something which tells you how specific a word is in a sense it is in it is basically just to a high degree really telling you how how rare a word is or how common a word is but as the 1st step that is probably for a prototype for next week that is probably not a bad thing i mean even if it is just that even if you have segments where lots of rare words occur highlighted in darker red than segments where all the very common words occur that is just that is somewhere to start from and it is a bit more sophisticated than that but then de facto it just ends up doing that mostly from what i figured out actually i think i am not going to start that now it is probably going to take too long so if we get it on a word by word basis whatever you do it will probably be on a word by word basis and what you hit us on a sort of segment but not quite segment base yes yeah i mean i have not really decided on how to really get the information out of this now because when i have for example increased speaker overlap that applies to several turns of course but i could give the information about how important this is to each of those segments okay what about the following model i mean this is a very unscientific way of doing it in some sense but what if we take time as the standard unit for now and sort of like make a massive 12nd split super array because everything you are doing can in one way or the other be tied down to actual time so if we if for each 12nd time slot we could attach a value and then it would be easy to then go back when if we have the time marks here and remap that on to on to the length of a segment you know what i mean so if you have for each word say and we know that word starts at this 2nd and ends at that 2nd and you have for like for some time period the overlap in that period and the f ones in that period or something so you also have a value which can be tied down to a time and then we could just in matlab or in something just create some massive super array of things for each for each like sort of time sampling slot and calculate the value for this and once we have this array we can go with the script and sort of go for each segment to the starting and end times and say okay this is from our time segment here to this time segment there so we take the sum of those and divide them by the number something create the average and put it in as the value for the segment you know what i mean but how can we get to know what makes sense as a function for joining everything together 0 it might be difficult to find out that is a fiddling play in the end but that problem we will always have because we do not have a useful way of automatically evaluating we do not have a way of usefully evaluating automatically what is good and what is bad so it is probably always going to be a question of looking at it and saying okay like running it with different factor loadings and seeing okay this way it works this well this way it works that well yeah 1st what you proposed we should break that down to have the smallest unity of time duration should be one word i mean that would be because that would be what naturally came out of her thing but it would probably be more difficult for map to map for you to map it on so for you to be sure but i could then if i have the value for a 2nd i would perhaps just give all the important word important for our you could always find out how many words there are in many yes yeah but so you say that in like you basically say having an array where each each cell is one like it is one word the thing and then you would map your information onto individual words yeah i would say somehow have to break it down i would probably somehow have to break it down to that level would you be able to find out which word that is yes sure okay and then we could have like some type of just point in the end where the one scores from all the individual word cells get multiplied all with like for each utterance or whatever you have get all multiplied with the same value all the ones that are within that utterance and that is sort of the combination of the 2 scores yeah and then we would have to go back again and then put that back into that segment mode here so that we because in the end we do not want it on a per word basis but probably on a per segment base yeah but then okay but at that moment it would be better for me to just make it on a per segment basis right way and that her adapted also to do 0 yeah actually that yeah that is true so that it is easier if you are able to yeah well i think that is probably back where we should back where we started with this but you said it is even more complicated because your segments are not those segments exactly yeah i mean mine are only more but they have the same starting end points sometimes there are 2 segments and one with a gap in between but they do not overlap and their segments do overlap no no they do not but those 2 or those do not i mean my segments overlap in the same way as theirs do but sometimes i have split one of the segments into 2 segments but it is easy to match the start and end times so you could on their granularity you could on their granularity create a score like for each of their segments well i guess i mean for you if you know for each word if you find that out then it has to be possible because if you know this is going from word to word or if this is going from time to time and then there has to be a way then for you say okay this concerns these following words and then just make a simple mean over them i think an interesting thing is if we do not combine your 2 scores in the xml file yet but if we do that in the software then we can probably make ways of playing with it in the software and sort of you know like adapt to the file some sort of control like playing around with looking like just playing with different weightings for the utterance based one versus the word based one and sort of look at it dynamically you know what i mean like playing around sort of figuring out what is the best way of combining them by playing around and looking at the results it could be quite difficult but yeah we could even have a look at our different measures if they come up with the same same same kinds of yeah we could probably like make a graphical display initially at least for experimenting which is placed them in different ways and then see how they interact with each other i mean that is what i am already doing with my okay so do you think like both of you then can map something onto their segments like just each of you provide one value like double value or whatever like one decimal value or whatever onto exactly their segments i think if i can provide something for the words as they are it is stated from where to where the segments go which it is it is stated both it is even which words it is it is done both in terms of words and in terms of sections it is a bit sad sort of that we do this before we have truly figured out how the nightex metal thing works because now we are doing it all by hand and parsing and un parsing that thing and it is all part of the framework framework yeah how much easier would it be if we truly understood this well it would be quite straightforward some of the tasks you had to do but no one really understands them but i might just change my order in which i do things and forget my latency stuff until the weekend and try to really make sense of the of the night data system now data system now so that maybe as soon as i have understood that we find ways of doing that in within the night framework already so that we do not manually have to parse times and and tie things together you mean by matching strings well at the moment what you would do like to solve this problem is you would sort of like write some pulse script or something that gets this time value out of here what i have done is a parser an xml parser where you can get the start times okay so that is quite easy because it is an attribute and you just say that you want the values of those attributes so provided that you get your words in the right order do you think it is an easy task for you it is a relatively feasible task for you to get just a single value per segment i think that should be possible i do not know how long it takes me okay and you say you think you are able as well to map onto those segments yeah and if you both able to map into those segments then we should be able to get one file where we have like wherever 2 values value a and value b both as attributes for this for this and that we could load into a prototype and see what types of what ways of displaying this information are there have some value quite soon that i just worked i mean i just calculated the values for the average of zeros and they are i mean i did not have a look at the data yet but they vary quite widely even for the same speaker across meetings so one speaker had an average of about 101 meeting and one meeting and 160 another meeting that is really strange i mean perhaps it is because of laughter or something that is what i was thinking but i did not have there is probably also social interaction factors in that there sometimes just a meeting like if people adapt their zeros to each other than sometimes yeah that is what i read in one of those papers yeah that could mess up things quite conservative do you not do something like just like not measuring the f 0 the absolute role but just like the variance of f 0 within a certain time frame and like sort of like just have some parts where it is very low variance where they are more less talking at the same f 0 and one where there is a lot of more variants i do not know about that yeah but all this is quite you know data intensive yeah i when i am let the just calculate the average of 0 levels it i think it needed more than half an hour considerably more than half an hour to do that for our meetings because yeah we have 75 hours per yeah average of 6 speakers and they measured every 0016 seconds and that gives us quite a lot of values okay so at the moment you are just you measuring the f 0s relative to the average for the speaker or yeah what i did at the moment is i yeah i got the f 0 values from for each speaker for his headphone and i only take those that were recorded at the time where he was actually speaking and for those i calculated the average so the average that gave me now one value per speaker per meeting okay so what you would be feeding in would be just like one value per speaker per meeting no no that is just the average that is your average baseline okay okay no yeah that makes a lot more sense so that would show you how much relative to how he is sort of how he is performing generally in that meeting relative to that how he is in a specific segment he or she yes i mean i just needed to have this way you know to relate yeah yeah and for you that would be quite easy to translate into those segments yeah i guess i have asked this question 15 times is laughter annotated at all because you could take that and out maybe yeah i mean i actually have to look at the data what causes these because it is quite funny to have a male speaking at 200 0 they do exist yeah sure but the castratikor of the international computer science institute 0 well i am afraid i have to go soon i am not quite sure but there is anything more we have to talk about anyway maybe as we meet at the weekend meet at the weekend yeah see as soon as i am halfway through my lsa like basically as soon as i have the matrix built of the document by word stuff it is very easy to then calculate for each word a score and i can just give you those scores and you can do with them whatever you want so how about the prototype if we want to show him something on monday we definitely have to work together some of us at least have to work together to do to work together to get the running yeah i think colin dave and me will actually work on the java stuff in it and we will just see whatever you supply us we will try to tie in and visualize in some way or another i will ask jonathan if we can postpone the meeting to 10 clock yeah so that would give us a chance of meeting for an hour before that to discuss the questions that we had yeah yeah you just send us an email and tell us what is happening yeah i have not gotten like sort of my confirmation that wednesday is fine i am not sure if i am supposed to expect a confirmation from my confirmation from him now but i will just email him again and ask him if we can maybe make it one sorry reset 12 and i am asking if you can make one this is a bit frustrating at the moment of this project is not it it is so like difficult to get to the point where you understand enough to really feel that i do not know i am not feeling that i am really working at the moment i am just trying to make sense of everything it is a bit too far into the meeting for that and to the project and i am at the moment feeling around with my data and not quite seeing how i get to a sensible abstraction level you know from my very i guess as soon as we have a framework in the type of the prototype where each of us can tie in their stuff and see how it looks like and how it performs you know that probably makes it a lot easier then but it is a bootstrapping problem like for the prototype we need some type of data but to develop the data it would be a lot easier to have the prototype anyway i got to go i mean you will be happy with some data even if it does not make much sense yes but if it is if it is in a form which is easy to read in at the moment that would be fine sort of like if basically if we have something like this segments file but for each of you like just have one attribute i think it is really easy if we do not merge them beforehand but if we let them if we combine them in the prototype or do not at the moment because then we can easily display them individually contrast them to each other and play around with how to combine them and also we do not have to recalculate it just exactly yeah yeah and i mean computationally multiplying 2 integers or doubles or whatever should not be the thing that if it is already in segment space that is not too much yeah all right i am going to be