We propose the task of turning point identification in movies as a means of analyzing their narrative structure . We argue that turning points and the segmentation they provide can facilitate processing long , complex narratives , such as screenplays , for summarization and question answering . We introduce a dataset consisting of screenplays and plot synopses annotated with turning points and present an end-to-end neural network model that identifies turning points in plot synopses and projects them onto scenes in screenplays . In natural language processing applications , such as those related to question answering systems , and more specifically , to semantic role labelling , an important task to perform during the text normalization phase is lemmatization which consists in determining those two words which have the same root , despite their surface differences . Due to the lack of a practical lemmatizing tool suitable for the Italian language ( which is a highly inflectional one ) , in this paper we aim to present LIT , a Rule based Italian lemmatizer consisting of a full rule-base lemmatization of all dictionary-words and a discovery algorithm which attempts to predict the grammar of neologisms . We assume that good teachers ask good ( and lots of ) questions of their students . Yet this general recommendation fails to consider important questions about how teachers implement question-asking . The purpose of this study is to think more deeply about the implementation of this important practice . Results show that parents and children engaged in high-quality interactions during coding app play , with parents and children exhibiting high responsiveness and task-relevant talk , and parents exhibiting a higher proportion of question-asking and spatial talk compared to their children . Importantly , linear regression analyses show that the dyad 's ability to stay on task during the coding task predicts children 's learning of coding , while question-asking was a negative predictor of children 's learning . These results suggest that coding apps may be a rich context for STEM learning , and that specific parent-child interactions can scaffold their children 's learning from STEM apps . Retrieving answer containing passages is a challenging task in Question Answer- ing . In this paper , we describe a novel passage retrieval methodology using answer type profiles . We study a question answering problem on a social network , where a requester is seeking an answer from the agents on the network . The goal is to design reward mechanisms to incentivize the agents to propagate the requester 's query to their neighbours if they do n't have the answer . The domain ontology ( i.e. , specification of domains , slots , and values ) of a conversational AI system is generally incomplete , making the capability for DST models to generalize to new slots , values , and domains during inference imperative . In this paper , we propose to model multi-domain DST as a question answering problem , referred to as Dialogue State Tracking via Question Answering ( DSTQA ) . Within DSTQA , each turn generates a question asking for the value of a ( domain , slot ) pair , thus making it naturally extensible to unseen domains , slots , and values . Different Decision Maker ( DMs ) think , learn , behave , and make decisions differently . Hence , one type of questioning/answering , procedure , or assumptions about the DM 's behavior may not sufficiently support DMs to learn about their preferences and make appropriate decisions . To provide a variety of flexible and applicable tools for the DM , a set of approaches are described that use different types and numbers of questions , different procedures to solve the problem , and different assumptions regarding the behavior of the DM . e-learning system utilizing the internet has created the open educational environment which can overcome restrictions in time and space . The system can provide students with instruction tailored to each of them , covering such aspects as attendance , learning , questioning/answering , homework and test . Despite many strong points of E-learning system , however , it involves quite a few problems to be rectified . In this paper , we propose two new methods targeting NTCIR-4 QAC2 . First , we use knowledge resembling “ common sense ” for question answering purposes . For example , the length of a runway in an airport must be a few kilometers , but a few centimeters . This work demonstrates Treo , a framework which converges elements from NLP , IR , SW and Databases , to create a semantic search engine and question answering ( QA ) system for heterogeneous data . Jeopardy and Question Answering queries over open domain structured and unstructured data are used to demonstrate the approach . In this work , Treo is extended to cope with unstructured text in addition to structured data . The goal of name disambiguation is to identify the different referents of a same name in corpus . Technology of name disambiguation can be applied in web searching , information extraction , translation system , questioning-answer system , and so on . The key part to solving name ambiguity problem is to model context of names and feature of candidates , and then measure similarity . section : Introduction Question answering has been a long - standing research problem . Recently , reading comprehension ( RC ) , a challenge to answer a question given textual evidence provided in a document set , has received much attention . It is therefore essential that interactive AI systems are capable of generating text textgen . A wide variety of applications rely on text generation , including machine translation , video / text summarization , question answering , among others . From a machine learning perspective , text generation is the problem of predicting a syntactically and semantically correct sequence of consecutive words given some context . Implementation details for the BiLSTM task architectures , as well as training details , are available in Appendix [ reference ] . paragraph : Question Answering We use 2 recent DQA benchmark training and evaluation datasets , SQuAD Rajpurkar2016 and TriviaQA JoshiTriviaQA2017 . subsection : Question Answering Table [ reference ] presents our results on two question answering benchmarks . The results demonstrate that the introduction of the refinement module helps consistently , and further improvements come from using common sense knowledge from ConceptNet ( ) . subsubsection : CNN based question model In this study , we also explore to use a CNN similar to for question representation . Similar to the LSTM - based question model , we first embed words to vectors and get the question vector by concatenating the word vectors : Then we apply convolution operation on the word embedding vectors . For convolution feature maps of different sizes , we concatenate them to form the feature representation vector of the whole question sentence : hence is the CNN based question vector . The diagram of CNN model for question is shown in Fig . [ reference ] . Finally , the sum of this evidence vector and the question embedding is used to predict the answer for the given image and question . For the question representation , we choose the bag - of - words ( BOW ) . Other question representations , such as an LSTM , can also be used , however , BOW has fewer parameters yet has shown good performance . Machine reading comprehension ( MRC ) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine . Compared with MRC on a single passage , multi - passage MRC is more challenging , since we are likely to get multiple confusing answer candidates from different passages . We used publicly available benchmark datasets from to evaluate EXAM . There are in total 6 text classification datasets , corresponding to sentiment analysis , news classification , question - answer and ontology extraction tasks , respectively . Table 1 shows the descriptive statistics of datasets used in our experiments . section : Neural Answer Selection Model Answer sentence selection is a question answering paradigm where a model must identify the correct sentences answering a factual question from a set of candidate sentences . Assume a question is associated with a set of answer sentences , together with their judgements , where if the answer is correct and otherwise . We present a new , simple model that uses attention to directly pick the answer from the context as opposed to computing the answer using a blended representation of words in the document as is usual in similar models . This makes the model particularly suitable for question - answering problems where the answer is a single word from the document . blackEnsemble of our models sets new state of the art on all evaluated datasets . paragraph : Binary and multi - class classification We use a set of binary classification tasks ( see Table [ reference ] ) that covers various types of sentence classification , including sentiment analysis ( MR , SST ) , question - type ( TREC ) , product reviews ( CR ) , subjectivity / objectivity ( SUBJ ) and opinion polarity ( MPQA ) . We generate sentence vectors and train a logistic regression on top . Such queries can best and often only be answered by humans that are currently on-site . Although online platforms for community question answering are very popular , very few exceptions consider the notion of users ’ current physical locations . In this article , we present CloseUp , our prototype for the seamless integration of community-driven live search into a Google-like search experience . The question classification gives the system the possibility to have information about question categories to focus on questions and answers from relevant categories to the input question . In this paper , we propose a new method based on unsupervised Latent Dirichlet Allocation for classifying questions in community-based question answering . Our method first uses unsupervised topic modeling to extract topics from a large amount of unlabeled data . Abstract Here , we propose a preference enhanced hybrid expertise retrieval ( PEHER ) system in community question answering services . PEHER consists of three segments , namely , preferability estimator , authority estimator , and expertise estimator . Both novice and expert users search the web exhaustively for their coding practises to learn gradations behind libraries , programming languages and frameworks.[1,2 ] In community-based question-answering communities , where students ask questions there is no guarantee that students get their what they are searching for . This poses an unsatisfactory level in the student . Besides watching videos , user participate in discussion forums to further their understanding of the course material . As in other community-based question-answering communities , in many MOOC forums a user posting a question can mark the answer they are most satisfied with . In this paper , we present a machine learning model that predicts this accepted answer to a forum question using historical forum data . In many questions in Community Question Answering sites users look for the advice or opinion of other users who might offer diverse perspectives on a topic at hand . The novel task we address is providing supportive evidence for human answers to such questions , which will potentially help the asker in choosing answers that fit her needs . Team formation in the setting of social networks is a fundamental problem in many database or web applications . For example , we need to find a suitable team to answer community-based questions and collaborative software development . It is also well-recognized that forming a suitable team in social networks is non-trivial , since the problem involves many cost factors such as communication overhead and load balancing . In this paper , we describe our submission to the NTCIR12 Short Text Conversation task . We consider short text conversation as a community Question-Answering problem , hence we solve this task in three steps : First , we retrieve a set of candidate posts from a pre-built indexing service . Second , these candidate posts are ranked according to their similarity with the original input post . Question Quality in Community Q&A. Asking the right question in the right way is an art ( and a science ) . In a community question-answering setting , a good question is not just one that is found to be useful by other people : a question is good if it is also presented clearly and shows prior research . Using a community question-answering site that allows voting over the questions , we show that there is a notion of question quality that goes beyond mere popularity . Answer Ranking in CQA . There is also previous research on ranking answers from community question answering ( CQA ) sites . Surdeanu et al . investigated a wide range of feature types such as similarity features , translation features , density / frequency features for ranking answers to non - factoid questions in Yahoo ! There are two popular types of text matching tasks in NLP . One is ranking task , such as community question answering . Another is classification task , such as textual entailment . The research can be broadly classified into the following areas : ( 1 ) compressed sensing , ( 2 ) sparse representation and encoding for digital elevation maps , ( 3 ) learning theory , and ( 4 ) high dimensional approximation . In addition to solving several fundamental mathematical questions in these areas , this work has developed numerical algorithms and software for encoding digital elevation maps which perform at the highest compression rates . Additionally , ground has been broken on new methods in the emerging field of compressed sensing . This study aims to find out to find out the difficulties experienced by students in mathematical problem solving questions and to find out the improvement of mathematical problem solving abilities of students using the Guided Inquiry learning model . Besides knowing the steps where students have difficulty in solving mathematical problem solving questions . The method used in this study is a qualitative method with descriptive research methods . The field comprises computational chemistry and molecular dynamics . A variety of mathematical and numerical questions raised is reviewed . Placing the models and the techniques employed for simulation on a firm mathematical ground is a difficult task , which has begun decades ago . Many mathematical and statistical questions require the use of computational assistance . The proposed XploRe Quantlet Client ( XQC ) with it 's combined CUI and GUI interface gives access to the XploRe software environment ( XploRe Quantlet Server – XQS ) with it 's large number of available mathematical and statistical methods ( Quantlets ) via the Internet . While several models have been proposed for text question answering , math question answering is in an earlier stage of development . To advance math-aware search and mathematical question answering systems , we will create a standard test collection for researchers to use for benchmarking . ARQMath will also include a formula retrieval sub-task : individual formulas from question posts are used to locate formulas in earlier answer posts , with relevance determined by narrative fields created based on the original question . Such concepts have a reputation of being difficult to learn and to teach . The specificity of such a situation is that it comes from discrete mathematics and it allows a mathematical questioning and a mathematical From the relationship between opening questions and closed questions and the relationship between macro_method of mathematical thought and elementary micro_knowledge as well as the stratification of mathematical exercises etc.this paper expounds the three major principles in designing mathematics opening questions in middle school . in mathematics knowledge occurrence , the developing process , in mathematics question solution process , utilizes mathematics experiment method appropriately , may make the student directly to observe , begins to operate , the thorough ponder analysis , to explore the research repeatedly personally . This article elaborated mathematics experiment teaching principle , the opportunity and the matters needing attention . We carefully perform variable control on 11.5 M questions from online forums to get a dataset , OQRanD , and further perform question analysis . Based on these conclusions , several models are built for question evaluation . For openQG task , we construct OQGenD , the first dataset as far as we know , and propose a model based on conditional generative adversarial networks and our question evaluation model . Objective type of Examination evaluation is easy in Computer world . But the descriptive type of question evaluation is more difficult and there is no significant research has been taken place . In this paper I propose a new solution to the above problem with text classification using the new fuzzy logic named CosFuzzy Logic . The instrument used to see the effect of virtual laboratory was a test instrument . While the instrument used to assess the quality of the media was a questionnaire assessment . The results of this study were a developed virtual laboratory which can be operated via web on smartphones and computers . Data collected is sourced from experts with different expertise . Assessing experts were asked to provide a questionnaire assessment related to the internal environment and the external environment of the clinic . Financial statements used are from 2015 to 2017 . Furthermore , empirical results also show that the elimination of stopwords leads to better correlation estimation between questions and leads to better categorization accuracy . On the other hand , incorporating students ' votes on the questions does not improve categorization accuracy , although a similar feature has been shown to be effective in community question answering environments for assessing question quality . Ten mobile applications ( 4 for iOS and 6 for Android ) are selected for the evaluation . A list of tasks is set to be executed in order to answer a quality assessment questionnaire that is developed for this purpose , covering the four quality characteristics : Functional suitability , Operability , Performance efficiency and Reliability , by using the ISO/IEC 25010 quality standard . This evaluation aims to analyze the degree of meeting these quality characteristics , in addition to the compliance between the users ' ratings in the apps stores and the fulfillment of the quality characteristics by the mPHRs for pregnancy monitoring . An External quality evaluation was carried out on 17 pregnancy monitoring mPHRs for iOS and Android . The evaluation consisted of performing a set of tasks and filling in a quality assessment questionnaire that covers the four External quality characteristics : Functional suitability , Operability , Performance efficiency and Reliability . Moreover , 14 mPHRs for pregnancy monitoring were used to study the relationship between the External quality and the Quality in-use by means of four classification techniques : Iterative Dichotomiser 3 ( ID3 ) , C4.5 , K-nearest neighbors ( K-NN ) and Naïve Bayes . Volume II .. This report documents the project management of Question Based Assessment in Tutorial Dialogues , which was initiated to provide a solution to evaluation needs of professional or educational training environments . The introduction presents the software , describes the background of the project which describes the motivation of the software project explaining that organisation may need a way to evaluate the focus ability their staff or student , the scope of the project . Remote sensing datasets are characterized by multiple types of imperfections that alter extracted information and taken decisions to a variable degree depending on data acquisition conditions , processing , and final product requirements . Therefore , regardless of the sensors , type of data , extracted information , and complementary algorithms , the quality assessment question is a pervading and particularly complex one . This chapter summarizes relevant quality assessment approaches that have been proposed for data acquisition , information extraction , and data and information fusion , of the remote sensing acquisition-decision process . This metamodel is capable of modeling complex activities , their interrelationships , the relationship of activities with data sources and execution details . The ex ante treatment of the metadata repository is enabled by a full set of steps , i.e. , quality question , which constitute our methodology for data warehouse quality management and the quality-oriented evolution of a data warehouse based on the architecture , process and quality metamodels . Our approach ex tends GQM , based on the idea that a goal is operationally defined over a set of questions . Now scientific papers ' evaluation is mechanically treated as a text assessment . The peer review relies a text assessment more and more , but not does the question assessment . The real purpose of scientific papers ' evaluation is neglected . These new multimedia features were not part of previous textbased research , and the objective of this project is to fill that research void : how does the use of multimedia in a question affect the odds of receiving the correct answer ? Our findings will not only affect this previous Q&A research on question quality and forwarding , but also reveal new security and privacy concerns . To perform our research , we created several different types of multimedia questions , spanning many different topics . Knowing question quality provides us with finding and recommending good questions together with identifying bad ones which hinder the CQA service . In this paper , we are conducting two studies to investigate the question quality issue . The first study analyzes the factors of question quality and finds that the interaction between askers and topics results in the differences of question quality .
We propose the task of turning point identification in movies as a means of analyzing their narrative structure . We argue that turning points and the segmentation they provide can facilitate processing long , complex narratives , such as screenplays , for summarization and question answering . We introduce a dataset consisting of screenplays and plot synopses annotated with turning points and present an end-to-end neural network model that identifies turning points in plot synopses and projects them onto scenes in screenplays .
In natural language processing applications , such as those related to question answering systems , and more specifically , to semantic role labelling , an important task to perform during the text normalization phase is lemmatization which consists in determining those two words which have the same root , despite their surface differences . Due to the lack of a practical lemmatizing tool suitable for the Italian language ( which is a highly inflectional one ) , in this paper we aim to present LIT , a Rule based Italian lemmatizer consisting of a full rule-base lemmatization of all dictionary-words and a discovery algorithm which attempts to predict the grammar of neologisms .
We assume that good teachers ask good ( and lots of ) questions of their students . Yet this general recommendation fails to consider important questions about how teachers implement question-asking . The purpose of this study is to think more deeply about the implementation of this important practice .
Results show that parents and children engaged in high-quality interactions during coding app play , with parents and children exhibiting high responsiveness and task-relevant talk , and parents exhibiting a higher proportion of question-asking and spatial talk compared to their children . Importantly , linear regression analyses show that the dyad 's ability to stay on task during the coding task predicts children 's learning of coding , while question-asking was a negative predictor of children 's learning . These results suggest that coding apps may be a rich context for STEM learning , and that specific parent-child interactions can scaffold their children 's learning from STEM apps .
Retrieving answer containing passages is a challenging task in Question Answer- ing . In this paper , we describe a novel passage retrieval methodology using answer type profiles .
We study a question answering problem on a social network , where a requester is seeking an answer from the agents on the network . The goal is to design reward mechanisms to incentivize the agents to propagate the requester 's query to their neighbours if they do n't have the answer .
The domain ontology ( i.e. , specification of domains , slots , and values ) of a conversational AI system is generally incomplete , making the capability for DST models to generalize to new slots , values , and domains during inference imperative . In this paper , we propose to model multi-domain DST as a question answering problem , referred to as Dialogue State Tracking via Question Answering ( DSTQA ) . Within DSTQA , each turn generates a question asking for the value of a ( domain , slot ) pair , thus making it naturally extensible to unseen domains , slots , and values .
Different Decision Maker ( DMs ) think , learn , behave , and make decisions differently . Hence , one type of questioning/answering , procedure , or assumptions about the DM 's behavior may not sufficiently support DMs to learn about their preferences and make appropriate decisions . To provide a variety of flexible and applicable tools for the DM , a set of approaches are described that use different types and numbers of questions , different procedures to solve the problem , and different assumptions regarding the behavior of the DM .
e-learning system utilizing the internet has created the open educational environment which can overcome restrictions in time and space . The system can provide students with instruction tailored to each of them , covering such aspects as attendance , learning , questioning/answering , homework and test . Despite many strong points of E-learning system , however , it involves quite a few problems to be rectified .
In this paper , we propose two new methods targeting NTCIR-4 QAC2 . First , we use knowledge resembling “ common sense ” for question answering purposes . For example , the length of a runway in an airport must be a few kilometers , but a few centimeters .
This work demonstrates Treo , a framework which converges elements from NLP , IR , SW and Databases , to create a semantic search engine and question answering ( QA ) system for heterogeneous data . Jeopardy and Question Answering queries over open domain structured and unstructured data are used to demonstrate the approach . In this work , Treo is extended to cope with unstructured text in addition to structured data .
The goal of name disambiguation is to identify the different referents of a same name in corpus . Technology of name disambiguation can be applied in web searching , information extraction , translation system , questioning-answer system , and so on . The key part to solving name ambiguity problem is to model context of names and feature of candidates , and then measure similarity .
section : Introduction Question answering has been a long - standing research problem . Recently , reading comprehension ( RC ) , a challenge to answer a question given textual evidence provided in a document set , has received much attention .
It is therefore essential that interactive AI systems are capable of generating text textgen . A wide variety of applications rely on text generation , including machine translation , video / text summarization , question answering , among others . From a machine learning perspective , text generation is the problem of predicting a syntactically and semantically correct sequence of consecutive words given some context .
Implementation details for the BiLSTM task architectures , as well as training details , are available in Appendix [ reference ] . paragraph : Question Answering We use 2 recent DQA benchmark training and evaluation datasets , SQuAD Rajpurkar2016 and TriviaQA JoshiTriviaQA2017 .
subsection : Question Answering Table [ reference ] presents our results on two question answering benchmarks . The results demonstrate that the introduction of the refinement module helps consistently , and further improvements come from using common sense knowledge from ConceptNet ( ) .
subsubsection : CNN based question model In this study , we also explore to use a CNN similar to for question representation . Similar to the LSTM - based question model , we first embed words to vectors and get the question vector by concatenating the word vectors : Then we apply convolution operation on the word embedding vectors .
For convolution feature maps of different sizes , we concatenate them to form the feature representation vector of the whole question sentence : hence is the CNN based question vector . The diagram of CNN model for question is shown in Fig . [ reference ] .
Finally , the sum of this evidence vector and the question embedding is used to predict the answer for the given image and question . For the question representation , we choose the bag - of - words ( BOW ) . Other question representations , such as an LSTM , can also be used , however , BOW has fewer parameters yet has shown good performance .
Machine reading comprehension ( MRC ) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine . Compared with MRC on a single passage , multi - passage MRC is more challenging , since we are likely to get multiple confusing answer candidates from different passages .
We used publicly available benchmark datasets from to evaluate EXAM . There are in total 6 text classification datasets , corresponding to sentiment analysis , news classification , question - answer and ontology extraction tasks , respectively . Table 1 shows the descriptive statistics of datasets used in our experiments .
section : Neural Answer Selection Model Answer sentence selection is a question answering paradigm where a model must identify the correct sentences answering a factual question from a set of candidate sentences . Assume a question is associated with a set of answer sentences , together with their judgements , where if the answer is correct and otherwise .
We present a new , simple model that uses attention to directly pick the answer from the context as opposed to computing the answer using a blended representation of words in the document as is usual in similar models . This makes the model particularly suitable for question - answering problems where the answer is a single word from the document . blackEnsemble of our models sets new state of the art on all evaluated datasets .
paragraph : Binary and multi - class classification We use a set of binary classification tasks ( see Table [ reference ] ) that covers various types of sentence classification , including sentiment analysis ( MR , SST ) , question - type ( TREC ) , product reviews ( CR ) , subjectivity / objectivity ( SUBJ ) and opinion polarity ( MPQA ) . We generate sentence vectors and train a logistic regression on top .
Such queries can best and often only be answered by humans that are currently on-site . Although online platforms for community question answering are very popular , very few exceptions consider the notion of users ’ current physical locations . In this article , we present CloseUp , our prototype for the seamless integration of community-driven live search into a Google-like search experience .
The question classification gives the system the possibility to have information about question categories to focus on questions and answers from relevant categories to the input question . In this paper , we propose a new method based on unsupervised Latent Dirichlet Allocation for classifying questions in community-based question answering . Our method first uses unsupervised topic modeling to extract topics from a large amount of unlabeled data .
Abstract Here , we propose a preference enhanced hybrid expertise retrieval ( PEHER ) system in community question answering services . PEHER consists of three segments , namely , preferability estimator , authority estimator , and expertise estimator .
Both novice and expert users search the web exhaustively for their coding practises to learn gradations behind libraries , programming languages and frameworks.[1,2 ] In community-based question-answering communities , where students ask questions there is no guarantee that students get their what they are searching for . This poses an unsatisfactory level in the student .
Besides watching videos , user participate in discussion forums to further their understanding of the course material . As in other community-based question-answering communities , in many MOOC forums a user posting a question can mark the answer they are most satisfied with . In this paper , we present a machine learning model that predicts this accepted answer to a forum question using historical forum data .
In many questions in Community Question Answering sites users look for the advice or opinion of other users who might offer diverse perspectives on a topic at hand . The novel task we address is providing supportive evidence for human answers to such questions , which will potentially help the asker in choosing answers that fit her needs .
Team formation in the setting of social networks is a fundamental problem in many database or web applications . For example , we need to find a suitable team to answer community-based questions and collaborative software development . It is also well-recognized that forming a suitable team in social networks is non-trivial , since the problem involves many cost factors such as communication overhead and load balancing .
In this paper , we describe our submission to the NTCIR12 Short Text Conversation task . We consider short text conversation as a community Question-Answering problem , hence we solve this task in three steps : First , we retrieve a set of candidate posts from a pre-built indexing service . Second , these candidate posts are ranked according to their similarity with the original input post .
Question Quality in Community Q&A. Asking the right question in the right way is an art ( and a science ) . In a community question-answering setting , a good question is not just one that is found to be useful by other people : a question is good if it is also presented clearly and shows prior research . Using a community question-answering site that allows voting over the questions , we show that there is a notion of question quality that goes beyond mere popularity .
Answer Ranking in CQA . There is also previous research on ranking answers from community question answering ( CQA ) sites . Surdeanu et al . investigated a wide range of feature types such as similarity features , translation features , density / frequency features for ranking answers to non - factoid questions in Yahoo !
There are two popular types of text matching tasks in NLP . One is ranking task , such as community question answering . Another is classification task , such as textual entailment .
The research can be broadly classified into the following areas : ( 1 ) compressed sensing , ( 2 ) sparse representation and encoding for digital elevation maps , ( 3 ) learning theory , and ( 4 ) high dimensional approximation . In addition to solving several fundamental mathematical questions in these areas , this work has developed numerical algorithms and software for encoding digital elevation maps which perform at the highest compression rates . Additionally , ground has been broken on new methods in the emerging field of compressed sensing .
This study aims to find out to find out the difficulties experienced by students in mathematical problem solving questions and to find out the improvement of mathematical problem solving abilities of students using the Guided Inquiry learning model . Besides knowing the steps where students have difficulty in solving mathematical problem solving questions . The method used in this study is a qualitative method with descriptive research methods .
The field comprises computational chemistry and molecular dynamics . A variety of mathematical and numerical questions raised is reviewed . Placing the models and the techniques employed for simulation on a firm mathematical ground is a difficult task , which has begun decades ago .
Many mathematical and statistical questions require the use of computational assistance . The proposed XploRe Quantlet Client ( XQC ) with it 's combined CUI and GUI interface gives access to the XploRe software environment ( XploRe Quantlet Server – XQS ) with it 's large number of available mathematical and statistical methods ( Quantlets ) via the Internet .
While several models have been proposed for text question answering , math question answering is in an earlier stage of development . To advance math-aware search and mathematical question answering systems , we will create a standard test collection for researchers to use for benchmarking . ARQMath will also include a formula retrieval sub-task : individual formulas from question posts are used to locate formulas in earlier answer posts , with relevance determined by narrative fields created based on the original question .
Such concepts have a reputation of being difficult to learn and to teach . The specificity of such a situation is that it comes from discrete mathematics and it allows a mathematical questioning and a mathematical
From the relationship between opening questions and closed questions and the relationship between macro_method of mathematical thought and elementary micro_knowledge as well as the stratification of mathematical exercises etc.this paper expounds the three major principles in designing mathematics opening questions in middle school .
in mathematics knowledge occurrence , the developing process , in mathematics question solution process , utilizes mathematics experiment method appropriately , may make the student directly to observe , begins to operate , the thorough ponder analysis , to explore the research repeatedly personally . This article elaborated mathematics experiment teaching principle , the opportunity and the matters needing attention .
We carefully perform variable control on 11.5 M questions from online forums to get a dataset , OQRanD , and further perform question analysis . Based on these conclusions , several models are built for question evaluation . For openQG task , we construct OQGenD , the first dataset as far as we know , and propose a model based on conditional generative adversarial networks and our question evaluation model .
Objective type of Examination evaluation is easy in Computer world . But the descriptive type of question evaluation is more difficult and there is no significant research has been taken place . In this paper I propose a new solution to the above problem with text classification using the new fuzzy logic named CosFuzzy Logic .
The instrument used to see the effect of virtual laboratory was a test instrument . While the instrument used to assess the quality of the media was a questionnaire assessment . The results of this study were a developed virtual laboratory which can be operated via web on smartphones and computers .
Data collected is sourced from experts with different expertise . Assessing experts were asked to provide a questionnaire assessment related to the internal environment and the external environment of the clinic . Financial statements used are from 2015 to 2017 .
Furthermore , empirical results also show that the elimination of stopwords leads to better correlation estimation between questions and leads to better categorization accuracy . On the other hand , incorporating students ' votes on the questions does not improve categorization accuracy , although a similar feature has been shown to be effective in community question answering environments for assessing question quality .
Ten mobile applications ( 4 for iOS and 6 for Android ) are selected for the evaluation . A list of tasks is set to be executed in order to answer a quality assessment questionnaire that is developed for this purpose , covering the four quality characteristics : Functional suitability , Operability , Performance efficiency and Reliability , by using the ISO/IEC 25010 quality standard . This evaluation aims to analyze the degree of meeting these quality characteristics , in addition to the compliance between the users ' ratings in the apps stores and the fulfillment of the quality characteristics by the mPHRs for pregnancy monitoring .
An External quality evaluation was carried out on 17 pregnancy monitoring mPHRs for iOS and Android . The evaluation consisted of performing a set of tasks and filling in a quality assessment questionnaire that covers the four External quality characteristics : Functional suitability , Operability , Performance efficiency and Reliability . Moreover , 14 mPHRs for pregnancy monitoring were used to study the relationship between the External quality and the Quality in-use by means of four classification techniques : Iterative Dichotomiser 3 ( ID3 ) , C4.5 , K-nearest neighbors ( K-NN ) and Naïve Bayes .
Volume II .. This report documents the project management of Question Based Assessment in Tutorial Dialogues , which was initiated to provide a solution to evaluation needs of professional or educational training environments . The introduction presents the software , describes the background of the project which describes the motivation of the software project explaining that organisation may need a way to evaluate the focus ability their staff or student , the scope of the project .
Remote sensing datasets are characterized by multiple types of imperfections that alter extracted information and taken decisions to a variable degree depending on data acquisition conditions , processing , and final product requirements . Therefore , regardless of the sensors , type of data , extracted information , and complementary algorithms , the quality assessment question is a pervading and particularly complex one . This chapter summarizes relevant quality assessment approaches that have been proposed for data acquisition , information extraction , and data and information fusion , of the remote sensing acquisition-decision process .
This metamodel is capable of modeling complex activities , their interrelationships , the relationship of activities with data sources and execution details . The ex ante treatment of the metadata repository is enabled by a full set of steps , i.e. , quality question , which constitute our methodology for data warehouse quality management and the quality-oriented evolution of a data warehouse based on the architecture , process and quality metamodels . Our approach ex tends GQM , based on the idea that a goal is operationally defined over a set of questions .
Now scientific papers ' evaluation is mechanically treated as a text assessment . The peer review relies a text assessment more and more , but not does the question assessment . The real purpose of scientific papers ' evaluation is neglected .
These new multimedia features were not part of previous textbased research , and the objective of this project is to fill that research void : how does the use of multimedia in a question affect the odds of receiving the correct answer ? Our findings will not only affect this previous Q&A research on question quality and forwarding , but also reveal new security and privacy concerns . To perform our research , we created several different types of multimedia questions , spanning many different topics .
Knowing question quality provides us with finding and recommending good questions together with identifying bad ones which hinder the CQA service . In this paper , we are conducting two studies to investigate the question quality issue . The first study analyzes the factors of question quality and finds that the interaction between askers and topics results in the differences of question quality .
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2019 2019 2011 2019 2009 2020 2019 1993 2008 2004 2013 2017 2019 2016 2018 2016 2016 2018 2019 2016 2016 2017 2019 2018 2020 2017 2016 2016 2016 2016 2014 2016 2016 2008 2019 2006 2003 2020 2010 2001 2009 2019 2013 2019 2019 2011 2016 2017 2007 2019 2000 2009 2015 2012
EMNLP/IJCNLP (1) IntelliSys (2) PACLIC NCM (1) NTCIR International Semantic Web Conference (Posters &amp; Demos) ACL ICLR 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV ACL AAAI ICML ACL EMNLP WWW (Companion Volume) ECIR NTCIR ICWSM CIKM EMNLP ECIR (2) ACL (1) HealthCom COMPSAC (1) Information Quality in Information Fusion and Decision Making WWW (Companion Volume)
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