This paper deals with the recognition of symbols and structural textures in architectural plans using string matching techniques . A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments . The string pattern matching algorithm is the key for intrusion detection . Several algorithms including BM , BMG , AC and AC-BM algorithm are discussed , the running time and memory consumption of these for algorithms are mea-sured by snort-based pattern matching algorithm on the snort intrusion detection system . Some heuristic rules were proposed to obtain training set for estimating the parameters of our retrieval model , and enabled the new model to do the whole retrieval process automatically . The experiment showed that the results of the new retrieval model have higher precision and recall than the string-match methods ' . We then perform a lexical analysis on the spoken words , emphasizing the use of pauses and utterance breaks , feeding that to a Support Vector Machine to test deceit or truth prediction . We then try out a method to incorporate utterance-based fusion of visual and lexical analysis , using string based matching . Preferred terms , their synonyms , broader , narrower , related terms , and captions are examined in combination with a stemmer and a stop-word list . The algorithm comprises string-to-string matching between words in the documents to be classified and words in term lists derived from the Ei thesaurus and classification scheme . The data collection for evaluation consists of some 35000 scientific paper abstracts from the compendex database . Again there are few research on n-gram string-similarity measures done on Malay . We have experimented on the application of stemming and string similarity matching on retrieving of verses from the Al-Quran in order to evaluated their effectiveness . Before retrieval effectiveness can be carried out an experimental data set need to be developed which comprises of a collection of documents , a set of queries , and their relevant judgements . In the production process of embedded device , due to the frequent reuse of third-party libraries or development kits , there are large number of same vulnerabilities that appear in more than one firmware . Homology analysis is often used in detecting this kind of vulnerabilities caused by code reuse or third-party reuse and in the homology analysis , the widely used methods are mainly Binary difference analysis , Normalized compression distance , String feature matching and Fuzz hash . But when we use these methods for homology analysis , we found that the detection result is not ideal and there is a high false positive rate . The United States Patent and Trademark Office does not provide unique inventor identifiers , however , making large-scale studies challenging . Many scholars of innovation have implemented ad-hoc disambiguation methods based on string similarity thresholds and string comparison matching ; such methods have been shown to be vulnerable to a number of problems that can adversely affect research results . The authors address this issue contributing ( 1 ) an application of the Author-ity disambiguation approach ( Torvik et al. , 2005 ; Torvik and Smalheiser , 2009 ) to the US utility patent database , ( 2 ) a new iterative blocking scheme that expands the match space of this algorithm while maintaining scalability , ( 3 ) a public posting of the algorithm and code , and ( 4 ) a public posting of the results of the algorithm in the form of a database of inventors and their associated patents . An optical computing method for string data matching applicable to genome information processing is presented . With encoding 1D data into spatial code patterns , moire fringes between two different coded patterns provide information on the correspondence of the original data , which is useful for string alignment in genome analysis . Note that on average each question has 2.70 unique answers for real images and 2.39 for abstract scenes . The agreement is significantly higher ( > 95 % ) for " yes / no " questions and lower for other questions ( < 76 % ) , possibly due to the fact that we perform exact string matching and do not account for synonyms , plurality , etc . Note that the automatic determination of synonyms is a difficult problem , since the level of answer granularity can vary across questions . We suppose that all potential answers are entities in the KB and that questions are sequences of words that include one identified KB entity . When this entity is not given , plain string matching is used to perform entity resolution . Smarter methods could be used but this is not our focus . Freebase data allows to train our model on 14 M questions but these have a fixed lexicon and vocabulary , which is not realistic . Following [ reference ] , we also created questions using ClueWeb extractions provided by [ reference ] . Using string matching , we ended up with 2 M extractions structured as ( subject , " text string " , object ) with both subject and object linked to Freebase . We also converted these triples into questions by using simple patterns and Freebase types . In this paper , we present an approach of merging spatial ontologies which consists of three processes : “ matching process ” , “ mapping process ” and “ merging process ” and we focus on the matching process . Indeed we consider three kind of matching : semantic matching , topologic matching and geometric matching . For each type of matching , we formally define functions for specifying correspondences between candidate concepts . We propose to measure performance risk according to the specific context and assessment criteria with the aid of a semantic similarity model for the SLA requirement being negotiated in a cloud computing environment . We show through simulations that the performance risk analysis is more accurate using semantic similarity matching compared with analysis without semantic similarity matching . Findings First , the semantic web services discovery reduces the number of relevant services through the application of matching algorithm “ semantic match ” . The result of this first matching restricts the search space at the level of UDDI registry , which allows the users to have good results for the “ functional match ” . Results also show that our proposed semantic clustered representation module improves the performance of models by up to 5.5 % Precision at 1 and 4.9 % Mean Average Precision . ConclusionsIn this paper , we introduce a large scale Chinese medical QA dataset and cast the task into a semantic matching problem . We also compare different CWS tools and input units . To solve this puzzle , one must understand the question to find out a correct answer . Semantic-based matching models rely mainly in semantic relatedness the input text words . We show that beyond the semantic similarities , matching models must focus on the most important words to find the correct answer . In addition to our innovative matching algorithm , we adopt an automating tuning for the various structural parameters used and propose a Mapping Expression model along with a collection of transformation operators . Hence , PLASMA enables a semantic matching process followed by a visual mapping process to optimize the translation process between exchanged documents or integrated schema . The invention discloses a text processing method and a text processing device . The text processing method comprises the following steps that S100 a relevance comparison table is generated through a semantic standardized table , a first standard library and a second standard library ; wherein table items of the semantic standardized table respectively comprise common terms and corresponding standardized terms ; S200 semantic standardized operation is carried out on a text built according to the first standard library through the semantic standardized table , and the text processed in a standardized mode is obtained ; S300 whether semantic accurate matching can be carried out on the text processed in the standardized mode and table items in the relevance comparison table is judged , if yes , the step S400 is executed , and if not , semantic fuzzy matching and/or manual matching is further executed , the semantic standardized table is updated according to the matching result and the step S200 is repeated ; S400 the text and/or a code processed in the standardized mode is replaced by a text of the second standard library corresponding to the matched table items in the relevance comparison table . The method was mainly divided into three steps , and each step was completed with the help of artificial intelligence . First , file information was preprocessed ( information processing and information filtering ) , then keywords were extracted from information content , and finally semantic similarity calculation and semantic information matching were conducted to complete key information retrieval in complex networks . The results showed that the accuracy of key information retrieval method of complex network based on artificial intelligence was improved by 2.27 % and the speed of retrieval was improved by 3.06 s. However , few or none of these tackle implementations issues related to XML messages management at the syntactic level . This article focuses on adapting some XML database solutions , specifically XML schema matching , to leverage the semantic matching analysis by providing a data transformation that enables data integration of exchanged messages . Our approach requires no semantic addition to already existing industrial engines to execute the resulting compositions . In order to pair short text fragments -- as a concatenation of separate words -- an adequate distributed sentence representation is needed , in existing literature often obtained by naively combining the individual word representations . We therefore investigated several text representations as a combination of word embeddings in the context of semantic pair matching . This paper investigates the effectiveness of several such naive techniques , as well as traditional tf-idf similarity , for fragments of different lengths . The results of the closely - related tasks ( “ AB ” , “ ABC ” , and “ DE ” ) show that our JMT model improves both of the high - level and low - level tasks . The results of “ JMT ” and “ JMT ” show that the parsing task can be improved by the semantic tasks . It should be noted that in our analysis on the greedy parsing results of the “ JMT ” setting , we have found that more than 95 % are well - formed dependency trees on the development set . In this paper , we use two kinds of semantic resources to recall more alignments , which include the similarity drawn from Glove embedding and the morphosemantic database in the WordNet project . Two additional matching schemes semantic match and morphological match are proposed as : paragraph : Semantic Match . Glove embedding encodes a word into its vector representation . subsection : Ablation To have a better understanding of our aligner , we conduct ablation test by removing the semantic matching and oracle parser tuning respectively and retrain the JAMR parser on the newswire proportion . The results are shown in Table [ reference ] . document : aNMM : Ranking Short Answer Texts with Attention - Based Neural Matching Model As an alternative to question answering methods based on feature engineering , deep learning approaches such as convolutional neural networks ( CNNs ) and Long Short - Term Memory Models ( LSTMs ) have recently been proposed for semantic matching of questions and answers . To achieve good results , however , these models have been combined with additional features such as word overlap or BM25 scores . Yih et . al . formulated answer sentence selection as a semantic matching problem with a latent word - alignment structure and conducted a series of experimental studies on leveraging proposed lexical semantic models . Iyyer et . Recently , Mitra et al . Mitra:2016 proposed to simultaneously learn local and distributional representations , which are early and late combination models respectively , to capture both exact term matching and semantic term matching . Until now , all the proposed neural models for ranking are trained on either explicit relevance judgements or clickthrough logs . However powerful attention mechanism is rarely used in these models . Inspired by the success of attention in machine translation and document classification , in this paper , we propose a Deep Hierarchical Attention Networks for Text Matching , namely Deep-HAN-Matching . Specifically , Deep-HAN-Matching extracts meaningful matching patterns and rich contextual features hierarchically from words to total document at the query term level using the recurrent neural network and attention mechanism , and finally rank the matching score produced by the fully connected neural network . Lightweight structure has three parts : a model of text structure as contiguous segments of text , or regions ; an extensible library of structure abstractions ( e.g. , HTML elements , Java expressions , or English sentences ) that can be implemented by any kind of pattern or parser ; and a region algebra that allows structure abstractions to be composed and reused . Lightweight structure does for text pattern matching what procedure abstraction does for programming , enabling the construction of a reusable pattern library . Lightweight structure has been implemented in LAPIS , a web browser/text editor that demonstrates several novel applications of the idea : • Text constraints is a new pattern language for composing structure abstractions , based on the region algebra . Recently , folksonomy-based image-sharing sites where users cooperatively make and utilize tags of image annotation have been gaining popularity . Typically , these sites retrieve images for a user request using simple text-based matching and display retrieved images in the form of photo stream . However , these tags are personal and subjective and images are not categorized , which results in poor retrieval accuracy and low user satisfaction . We review the current schemes of text-image matching models and propose improvements for both training and inference . First , we empirically show limitations of two popular loss ( sum and max-margin loss ) widely used in training text-image embeddings and propose a trade-off : a kNN-margin loss which 1 ) utilizes information from hard negatives and 2 ) is robust to noise as all $ K$-most hardest samples are taken into account , tolerating \emph{pseudo } negatives and outliers . When integrating information from multiple websites , the same data objects can exist in inconsistent text formats across sites , making it difficult to identify matching objects using exact text match . We have developed an object identification system called Active Atlas , which compares the objects ' shared attributes in order to identify matching objects . Given a large dataset of question-answer pairs , the problem can be tackled using text matching in two steps : find a set of similar questions to a given query from the dataset and then provide an answer to the query by evaluating the answers stored in the dataset for those questions . In this paper , we treat the text matching problem as an instance of the inexact graph matching problem and propose an efficient approximate matching scheme . We utilize the well known quadratic optimization problem metric labeling as the framework of graph matching . The invention discloses a text similarity matching method on the basis of a vector space model . The text similarity matching method includes extracting keywords of texts , clustering all the keywords and generating a keyword concept tree ; and computing the similarity of the texts according to the created keyword concept tree of the keywords in the texts to be translated , and acquiring texts in a translation depository according to the similarity . The texts in the translation depository are matched with the texts to be translated . Conclusions Health researchers exploring ways to use Twitter data for disease surveillance should be aware that the majority of tweets are not currently associated with an identifiable geographic location . Location can be identified for approximately 4 times the number of tweets using a straightforward text-matching process compared to using the GPS location information available in Twitter . Given the strong correlation between both data gathering methods , future research may consider using more qualitative approaches with higher yields , such as text mining , to acquire information about Twitter users ’ geographical location . Semantic search or text-to-video search in video is a novel and challenging problem in information and multimedia retrieval . Existing solutions are mainly limited to text-to-text matching , in which the query words are matched against the user-generated metadata . This kind of text-to-text search , though simple , is of limited functionality as it provides no understanding about the video content . These techniques include a new free-text predicate indexing method called the trie filter , and optimization techniques for choosing the best predicates and keywords for indexing . We introduce a new text matching function called tm_contains ( ) that allows users to specify text conditions using a Boolean , weighted Boolean , or vector space model . This function is analogous to the text matching user-defined routines available for current objectrelational database products . Distributed representations of words or sentences have been widely used in many natural language processing ( NLP ) tasks , such as text classification , question answering and machine translation and so on . Among these tasks , a common problem is modelling the relevance / similarity of the sentence pair , which is also called text semantic matching . Recently , deep learning based models is rising a substantial interest in text semantic matching and have achieved some great progresses . The output layer depends on the types of the tasks , we choose the corresponding form of output layer . There are two popular types of text matching tasks in NLP . One is ranking task , such as community question answering . They annotated passage - level answers , revisited several passage retrieval models with this data , and came to the conclusion that the current methods are not effective for this task . Yang et al . developed effective methods for answer sentence retrieval using this annotated data by combining semantic features , context features and basic text matching features with a learning to rank approach . Our model is built on attention - based neural matching model with value - shared weighting schema . From the word - level representation , it computes an interaction matrix , in which each entry is the matching score between a word and a class ( dot - product between their representations ) , illustrating the word - level matching signals . By taking the interaction matrix as a text representation , the later classification layer could incorporate fine - grained word level signals for the finer classification rather than simply making the text - level matching . Based upon the interaction mechanism , we devise an EXplicit interAction Model ( dubbed as EXAM ) . After recognizing text from image by OCR , it will store that message in any file . After that we used Boyer-Moore string search algorithm , for searching string stored in file . In this paper we analyze the worst case performance of the Boyer-Moore string searching algorithm [ 2 ] . We work within the paradigm of algorithmic analysis initiated by Knuth , that is we develop a mathematical theory dealing with the behavior of a specific ( and practical ) algori thm . We study Boyer-Moore-type string searching algorithms . First , we analyze the Horspool ’s variant . We describe a mechanically checked proof that a straightforward implementation of the preprocessing for a version of the Boyer-Moore Fast String Searching Algorithm is correct . We say “ straightforward ” because the implementation does not attempt to do the preprocessing quickly ( unlike production implementations ) . Nevertheless , in the modern computing environments , in which the amount of data to be searched trough is increasingly growing , the problem re-arises demanding for the different type of approaches that could target multi-gigabit throughput rates so as to perform close to real-time string lookups . In that light , this paper studies the potential of migrating , a well-known and widely used , Boyer-Moore string lookup algorithm to a hardware specific device capable of satisfying the demanded throughput , by proposing and characterising the initial implementation option on a reconfigurable platform . This paper investigates generalizations of the Boyer-Moore string pattern matching algorithm to parameterized pattern matching . Parameterized pattern matching was invented by Baker [ Bak93b ] for the purpose of finding sections of code in a software system that are the same except for a systematic change of parameters . The application contains description of selected proverbs , synonym and antonym in Malay stored in a text database . Retrieval of a specific word or alphabet based on the query is performed using Boyer-Moore String search concept . UML is used as the methodology in developing this application and Visual Basic version 6.0 is used as the programming language . The structure is particularly well suited for functions that are bottom on most of their domain . We then show how to use our methods to store in a compact way the bad-character shift function for variants of the Boyer-Moore text search algorithms . As a result , we obtain practical implementations of these algorithms that can be used with large alphabets , such as Unicode collation elements , with a small setup time . We propose to employ learnable text distance functions for each database field , and show that such measures are capable of adapting to the specific notion of similarity that is appropriate for the field ’s domain . We present two learnable text similarity measures suitable for this task : an extended variant of learnable string edit distance , and a novel vector-space based measure that employs a Support Vector Machine ( SVM ) for training . Experimental results on a range of datasets show We study the problem of how to match a formally structured restaurant menu item to a large database of less structured food items that has been collected via crowd-sourcing . At first glance , this problem scenario looks like a typical text matching problem that might possibly be solved with existing text similarity learning approaches . However , due to the unique nature of our scenario and the need for scalability , our problem imposes certain restrictions on possible machine learning approaches that we can employ . Recommendation systems are achieving great success in e-Commerce applications , during a live interaction with a customer ; recommendation system may apply different techniques to solve the problem of making a correct and relevant product recommendation . The main objective of this research is to perform product recommendation using textual similarity based Learning model . In this research data acquired through Amazon product advertising API after Data cleaning and text preprocessing the content based product recommendation have been performed using Bag of Words(BOW ) and Term Frequency-Inverse Document Frequency(TF-IDF ) based text vectorization techniques . matching is a classical problem in computer science . After the study of the Naive string search , Brute Force and the KMP algorithm , several advantages and disadvantages of the algorithms have been analyzed . Considering KMP in particular concept of parallelization has been introduced to improve the performance of the KMP algorithm . Next , we rank partially matched models for selection using semantic-based model attributes and behavior . Experiments conducted on a repository with 4,000 models show that on average DHT-based model lookup using production strings takes less than one millisecond compared with two minutes using naive string comparisons . Lastly , efficient model selection is a tradeoff between query representation and the computation cost of model ranking . Currently a large percentage of Internet traf£c consists of music £ les , typically stored in MP3 compressed audio format , shared and exchanged over Peer-to-Peer ( P2P ) networks . Searching for music is performed by specifying keywords and naive string matching techniques . In the past years the emerging research area of Music Information Retrieval ( MIR ) has produced a variety of new ways of looking at the problem of music search . The best known solution to date is due to Linhart and Shamir ( 2009 ) . Another broad yet comparatively less intensively studied class of string matching problems is numerical string searching , such as for instance ' ' less-than ' ' or L"1-norm string searching . The best known solutions for problems in this class are based on FFT convolution after some suitable re-encoding .
This paper deals with the recognition of symbols and structural textures in architectural plans using string matching techniques . A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments .
The string pattern matching algorithm is the key for intrusion detection . Several algorithms including BM , BMG , AC and AC-BM algorithm are discussed , the running time and memory consumption of these for algorithms are mea-sured by snort-based pattern matching algorithm on the snort intrusion detection system .
Some heuristic rules were proposed to obtain training set for estimating the parameters of our retrieval model , and enabled the new model to do the whole retrieval process automatically . The experiment showed that the results of the new retrieval model have higher precision and recall than the string-match methods ' .
We then perform a lexical analysis on the spoken words , emphasizing the use of pauses and utterance breaks , feeding that to a Support Vector Machine to test deceit or truth prediction . We then try out a method to incorporate utterance-based fusion of visual and lexical analysis , using string based matching .
Preferred terms , their synonyms , broader , narrower , related terms , and captions are examined in combination with a stemmer and a stop-word list . The algorithm comprises string-to-string matching between words in the documents to be classified and words in term lists derived from the Ei thesaurus and classification scheme . The data collection for evaluation consists of some 35000 scientific paper abstracts from the compendex database .
Again there are few research on n-gram string-similarity measures done on Malay . We have experimented on the application of stemming and string similarity matching on retrieving of verses from the Al-Quran in order to evaluated their effectiveness . Before retrieval effectiveness can be carried out an experimental data set need to be developed which comprises of a collection of documents , a set of queries , and their relevant judgements .
In the production process of embedded device , due to the frequent reuse of third-party libraries or development kits , there are large number of same vulnerabilities that appear in more than one firmware . Homology analysis is often used in detecting this kind of vulnerabilities caused by code reuse or third-party reuse and in the homology analysis , the widely used methods are mainly Binary difference analysis , Normalized compression distance , String feature matching and Fuzz hash . But when we use these methods for homology analysis , we found that the detection result is not ideal and there is a high false positive rate .
The United States Patent and Trademark Office does not provide unique inventor identifiers , however , making large-scale studies challenging . Many scholars of innovation have implemented ad-hoc disambiguation methods based on string similarity thresholds and string comparison matching ; such methods have been shown to be vulnerable to a number of problems that can adversely affect research results . The authors address this issue contributing ( 1 ) an application of the Author-ity disambiguation approach ( Torvik et al. , 2005 ; Torvik and Smalheiser , 2009 ) to the US utility patent database , ( 2 ) a new iterative blocking scheme that expands the match space of this algorithm while maintaining scalability , ( 3 ) a public posting of the algorithm and code , and ( 4 ) a public posting of the results of the algorithm in the form of a database of inventors and their associated patents .
An optical computing method for string data matching applicable to genome information processing is presented . With encoding 1D data into spatial code patterns , moire fringes between two different coded patterns provide information on the correspondence of the original data , which is useful for string alignment in genome analysis .
Note that on average each question has 2.70 unique answers for real images and 2.39 for abstract scenes . The agreement is significantly higher ( > 95 % ) for " yes / no " questions and lower for other questions ( < 76 % ) , possibly due to the fact that we perform exact string matching and do not account for synonyms , plurality , etc . Note that the automatic determination of synonyms is a difficult problem , since the level of answer granularity can vary across questions .
We suppose that all potential answers are entities in the KB and that questions are sequences of words that include one identified KB entity . When this entity is not given , plain string matching is used to perform entity resolution . Smarter methods could be used but this is not our focus .
Freebase data allows to train our model on 14 M questions but these have a fixed lexicon and vocabulary , which is not realistic . Following [ reference ] , we also created questions using ClueWeb extractions provided by [ reference ] . Using string matching , we ended up with 2 M extractions structured as ( subject , " text string " , object ) with both subject and object linked to Freebase . We also converted these triples into questions by using simple patterns and Freebase types .
In this paper , we present an approach of merging spatial ontologies which consists of three processes : “ matching process ” , “ mapping process ” and “ merging process ” and we focus on the matching process . Indeed we consider three kind of matching : semantic matching , topologic matching and geometric matching . For each type of matching , we formally define functions for specifying correspondences between candidate concepts .
We propose to measure performance risk according to the specific context and assessment criteria with the aid of a semantic similarity model for the SLA requirement being negotiated in a cloud computing environment . We show through simulations that the performance risk analysis is more accurate using semantic similarity matching compared with analysis without semantic similarity matching .
Findings First , the semantic web services discovery reduces the number of relevant services through the application of matching algorithm “ semantic match ” . The result of this first matching restricts the search space at the level of UDDI registry , which allows the users to have good results for the “ functional match ” .
Results also show that our proposed semantic clustered representation module improves the performance of models by up to 5.5 % Precision at 1 and 4.9 % Mean Average Precision . ConclusionsIn this paper , we introduce a large scale Chinese medical QA dataset and cast the task into a semantic matching problem . We also compare different CWS tools and input units .
To solve this puzzle , one must understand the question to find out a correct answer . Semantic-based matching models rely mainly in semantic relatedness the input text words . We show that beyond the semantic similarities , matching models must focus on the most important words to find the correct answer .
In addition to our innovative matching algorithm , we adopt an automating tuning for the various structural parameters used and propose a Mapping Expression model along with a collection of transformation operators . Hence , PLASMA enables a semantic matching process followed by a visual mapping process to optimize the translation process between exchanged documents or integrated schema .
The invention discloses a text processing method and a text processing device . The text processing method comprises the following steps that S100 a relevance comparison table is generated through a semantic standardized table , a first standard library and a second standard library ; wherein table items of the semantic standardized table respectively comprise common terms and corresponding standardized terms ; S200 semantic standardized operation is carried out on a text built according to the first standard library through the semantic standardized table , and the text processed in a standardized mode is obtained ; S300 whether semantic accurate matching can be carried out on the text processed in the standardized mode and table items in the relevance comparison table is judged , if yes , the step S400 is executed , and if not , semantic fuzzy matching and/or manual matching is further executed , the semantic standardized table is updated according to the matching result and the step S200 is repeated ; S400 the text and/or a code processed in the standardized mode is replaced by a text of the second standard library corresponding to the matched table items in the relevance comparison table .
The method was mainly divided into three steps , and each step was completed with the help of artificial intelligence . First , file information was preprocessed ( information processing and information filtering ) , then keywords were extracted from information content , and finally semantic similarity calculation and semantic information matching were conducted to complete key information retrieval in complex networks . The results showed that the accuracy of key information retrieval method of complex network based on artificial intelligence was improved by 2.27 % and the speed of retrieval was improved by 3.06 s.
However , few or none of these tackle implementations issues related to XML messages management at the syntactic level . This article focuses on adapting some XML database solutions , specifically XML schema matching , to leverage the semantic matching analysis by providing a data transformation that enables data integration of exchanged messages . Our approach requires no semantic addition to already existing industrial engines to execute the resulting compositions .
In order to pair short text fragments -- as a concatenation of separate words -- an adequate distributed sentence representation is needed , in existing literature often obtained by naively combining the individual word representations . We therefore investigated several text representations as a combination of word embeddings in the context of semantic pair matching . This paper investigates the effectiveness of several such naive techniques , as well as traditional tf-idf similarity , for fragments of different lengths .
The results of the closely - related tasks ( “ AB ” , “ ABC ” , and “ DE ” ) show that our JMT model improves both of the high - level and low - level tasks . The results of “ JMT ” and “ JMT ” show that the parsing task can be improved by the semantic tasks . It should be noted that in our analysis on the greedy parsing results of the “ JMT ” setting , we have found that more than 95 % are well - formed dependency trees on the development set .
In this paper , we use two kinds of semantic resources to recall more alignments , which include the similarity drawn from Glove embedding and the morphosemantic database in the WordNet project . Two additional matching schemes semantic match and morphological match are proposed as : paragraph : Semantic Match . Glove embedding encodes a word into its vector representation .
subsection : Ablation To have a better understanding of our aligner , we conduct ablation test by removing the semantic matching and oracle parser tuning respectively and retrain the JAMR parser on the newswire proportion . The results are shown in Table [ reference ] .
document : aNMM : Ranking Short Answer Texts with Attention - Based Neural Matching Model As an alternative to question answering methods based on feature engineering , deep learning approaches such as convolutional neural networks ( CNNs ) and Long Short - Term Memory Models ( LSTMs ) have recently been proposed for semantic matching of questions and answers . To achieve good results , however , these models have been combined with additional features such as word overlap or BM25 scores .
Yih et . al . formulated answer sentence selection as a semantic matching problem with a latent word - alignment structure and conducted a series of experimental studies on leveraging proposed lexical semantic models . Iyyer et .
Recently , Mitra et al . Mitra:2016 proposed to simultaneously learn local and distributional representations , which are early and late combination models respectively , to capture both exact term matching and semantic term matching . Until now , all the proposed neural models for ranking are trained on either explicit relevance judgements or clickthrough logs .
However powerful attention mechanism is rarely used in these models . Inspired by the success of attention in machine translation and document classification , in this paper , we propose a Deep Hierarchical Attention Networks for Text Matching , namely Deep-HAN-Matching . Specifically , Deep-HAN-Matching extracts meaningful matching patterns and rich contextual features hierarchically from words to total document at the query term level using the recurrent neural network and attention mechanism , and finally rank the matching score produced by the fully connected neural network .
Lightweight structure has three parts : a model of text structure as contiguous segments of text , or regions ; an extensible library of structure abstractions ( e.g. , HTML elements , Java expressions , or English sentences ) that can be implemented by any kind of pattern or parser ; and a region algebra that allows structure abstractions to be composed and reused . Lightweight structure does for text pattern matching what procedure abstraction does for programming , enabling the construction of a reusable pattern library . Lightweight structure has been implemented in LAPIS , a web browser/text editor that demonstrates several novel applications of the idea : • Text constraints is a new pattern language for composing structure abstractions , based on the region algebra .
Recently , folksonomy-based image-sharing sites where users cooperatively make and utilize tags of image annotation have been gaining popularity . Typically , these sites retrieve images for a user request using simple text-based matching and display retrieved images in the form of photo stream . However , these tags are personal and subjective and images are not categorized , which results in poor retrieval accuracy and low user satisfaction .
We review the current schemes of text-image matching models and propose improvements for both training and inference . First , we empirically show limitations of two popular loss ( sum and max-margin loss ) widely used in training text-image embeddings and propose a trade-off : a kNN-margin loss which 1 ) utilizes information from hard negatives and 2 ) is robust to noise as all $ K$-most hardest samples are taken into account , tolerating \emph{pseudo } negatives and outliers .
When integrating information from multiple websites , the same data objects can exist in inconsistent text formats across sites , making it difficult to identify matching objects using exact text match . We have developed an object identification system called Active Atlas , which compares the objects ' shared attributes in order to identify matching objects .
Given a large dataset of question-answer pairs , the problem can be tackled using text matching in two steps : find a set of similar questions to a given query from the dataset and then provide an answer to the query by evaluating the answers stored in the dataset for those questions . In this paper , we treat the text matching problem as an instance of the inexact graph matching problem and propose an efficient approximate matching scheme . We utilize the well known quadratic optimization problem metric labeling as the framework of graph matching .
The invention discloses a text similarity matching method on the basis of a vector space model . The text similarity matching method includes extracting keywords of texts , clustering all the keywords and generating a keyword concept tree ; and computing the similarity of the texts according to the created keyword concept tree of the keywords in the texts to be translated , and acquiring texts in a translation depository according to the similarity . The texts in the translation depository are matched with the texts to be translated .
Conclusions Health researchers exploring ways to use Twitter data for disease surveillance should be aware that the majority of tweets are not currently associated with an identifiable geographic location . Location can be identified for approximately 4 times the number of tweets using a straightforward text-matching process compared to using the GPS location information available in Twitter . Given the strong correlation between both data gathering methods , future research may consider using more qualitative approaches with higher yields , such as text mining , to acquire information about Twitter users ’ geographical location .
Semantic search or text-to-video search in video is a novel and challenging problem in information and multimedia retrieval . Existing solutions are mainly limited to text-to-text matching , in which the query words are matched against the user-generated metadata . This kind of text-to-text search , though simple , is of limited functionality as it provides no understanding about the video content .
These techniques include a new free-text predicate indexing method called the trie filter , and optimization techniques for choosing the best predicates and keywords for indexing . We introduce a new text matching function called tm_contains ( ) that allows users to specify text conditions using a Boolean , weighted Boolean , or vector space model . This function is analogous to the text matching user-defined routines available for current objectrelational database products .
Distributed representations of words or sentences have been widely used in many natural language processing ( NLP ) tasks , such as text classification , question answering and machine translation and so on . Among these tasks , a common problem is modelling the relevance / similarity of the sentence pair , which is also called text semantic matching . Recently , deep learning based models is rising a substantial interest in text semantic matching and have achieved some great progresses .
The output layer depends on the types of the tasks , we choose the corresponding form of output layer . There are two popular types of text matching tasks in NLP . One is ranking task , such as community question answering .
They annotated passage - level answers , revisited several passage retrieval models with this data , and came to the conclusion that the current methods are not effective for this task . Yang et al . developed effective methods for answer sentence retrieval using this annotated data by combining semantic features , context features and basic text matching features with a learning to rank approach . Our model is built on attention - based neural matching model with value - shared weighting schema .
From the word - level representation , it computes an interaction matrix , in which each entry is the matching score between a word and a class ( dot - product between their representations ) , illustrating the word - level matching signals . By taking the interaction matrix as a text representation , the later classification layer could incorporate fine - grained word level signals for the finer classification rather than simply making the text - level matching . Based upon the interaction mechanism , we devise an EXplicit interAction Model ( dubbed as EXAM ) .
After recognizing text from image by OCR , it will store that message in any file . After that we used Boyer-Moore string search algorithm , for searching string stored in file .
In this paper we analyze the worst case performance of the Boyer-Moore string searching algorithm [ 2 ] . We work within the paradigm of algorithmic analysis initiated by Knuth , that is we develop a mathematical theory dealing with the behavior of a specific ( and practical ) algori thm .
We study Boyer-Moore-type string searching algorithms . First , we analyze the Horspool ’s variant .
We describe a mechanically checked proof that a straightforward implementation of the preprocessing for a version of the Boyer-Moore Fast String Searching Algorithm is correct . We say “ straightforward ” because the implementation does not attempt to do the preprocessing quickly ( unlike production implementations ) .
Nevertheless , in the modern computing environments , in which the amount of data to be searched trough is increasingly growing , the problem re-arises demanding for the different type of approaches that could target multi-gigabit throughput rates so as to perform close to real-time string lookups . In that light , this paper studies the potential of migrating , a well-known and widely used , Boyer-Moore string lookup algorithm to a hardware specific device capable of satisfying the demanded throughput , by proposing and characterising the initial implementation option on a reconfigurable platform .
This paper investigates generalizations of the Boyer-Moore string pattern matching algorithm to parameterized pattern matching . Parameterized pattern matching was invented by Baker [ Bak93b ] for the purpose of finding sections of code in a software system that are the same except for a systematic change of parameters .
The application contains description of selected proverbs , synonym and antonym in Malay stored in a text database . Retrieval of a specific word or alphabet based on the query is performed using Boyer-Moore String search concept . UML is used as the methodology in developing this application and Visual Basic version 6.0 is used as the programming language .
The structure is particularly well suited for functions that are bottom on most of their domain . We then show how to use our methods to store in a compact way the bad-character shift function for variants of the Boyer-Moore text search algorithms . As a result , we obtain practical implementations of these algorithms that can be used with large alphabets , such as Unicode collation elements , with a small setup time .
We propose to employ learnable text distance functions for each database field , and show that such measures are capable of adapting to the specific notion of similarity that is appropriate for the field ’s domain . We present two learnable text similarity measures suitable for this task : an extended variant of learnable string edit distance , and a novel vector-space based measure that employs a Support Vector Machine ( SVM ) for training . Experimental results on a range of datasets show
We study the problem of how to match a formally structured restaurant menu item to a large database of less structured food items that has been collected via crowd-sourcing . At first glance , this problem scenario looks like a typical text matching problem that might possibly be solved with existing text similarity learning approaches . However , due to the unique nature of our scenario and the need for scalability , our problem imposes certain restrictions on possible machine learning approaches that we can employ .
Recommendation systems are achieving great success in e-Commerce applications , during a live interaction with a customer ; recommendation system may apply different techniques to solve the problem of making a correct and relevant product recommendation . The main objective of this research is to perform product recommendation using textual similarity based Learning model . In this research data acquired through Amazon product advertising API after Data cleaning and text preprocessing the content based product recommendation have been performed using Bag of Words(BOW ) and Term Frequency-Inverse Document Frequency(TF-IDF ) based text vectorization techniques .
matching is a classical problem in computer science . After the study of the Naive string search , Brute Force and the KMP algorithm , several advantages and disadvantages of the algorithms have been analyzed . Considering KMP in particular concept of parallelization has been introduced to improve the performance of the KMP algorithm .
Next , we rank partially matched models for selection using semantic-based model attributes and behavior . Experiments conducted on a repository with 4,000 models show that on average DHT-based model lookup using production strings takes less than one millisecond compared with two minutes using naive string comparisons . Lastly , efficient model selection is a tradeoff between query representation and the computation cost of model ranking .
Currently a large percentage of Internet traf£c consists of music £ les , typically stored in MP3 compressed audio format , shared and exchanged over Peer-to-Peer ( P2P ) networks . Searching for music is performed by specifying keywords and naive string matching techniques . In the past years the emerging research area of Music Information Retrieval ( MIR ) has produced a variety of new ways of looking at the problem of music search .
The best known solution to date is due to Linhart and Shamir ( 2009 ) . Another broad yet comparatively less intensively studied class of string matching problems is numerical string searching , such as for instance ' ' less-than ' ' or L"1-norm string searching . The best known solutions for problems in this class are based on FFT convolution after some suitable re-encoding .
A String Based Method to Recognize Symbols and Structural Textures in Architectural Plans Comparison of Several Pattern Matching Algorithms Based on Snort Modified BIR model and its application in FTP file retrieval The Truth and Nothing But the Truth: Multimodal Analysis for Deception Detection The Role of Different Thesauri Terms and Captions in Automated Subject Classification Experiments in malay information retrieval An Experimental Study of Four Methods for Homology Analysis of Firmware Vulnerability Disambiguation and co-authorship networks of the U.S. patent inventor database (1975–2010) String data matching based on a moire technique using 1D spatial coded patterns VQA: Visual Question Answering Question Answering with Subgraph Embeddings Matching Spatial Ontologies - A Challenge of Formalization Semantic Similarity Model for Risk Assessment in Forming Cloud Computing SLAs A semantic web services discovery approach based on a mobile agent using metadata Applying deep matching networks to Chinese medical question answering: a study and a dataset Asymmetry Sensitive Architecture for Neural Text Matching PLASMA: A Platform for Schema Matching and Management Text processing method and text processing device Research on Key Information Retrieval Method of Complex Network Based on Artificial Intelligence Syntax-Directed Data Integration in Semantic Web Services Orchestrations Learning Semantic Similarity for Very Short Texts A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks An AMR Aligner Tuned by Transition-based Parser aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model Neural Ranking Models with Weak Supervision Deep Hierarchical Attention Networks for Text Matching in Information Retrieval A Faster Data Structure and Algorithm for the Machine Learning System Gandalf A Categorization Scheme of Tag-based Folksonomy Images for Efficient Image Retrieval A Strong and Robust Baseline for Text-Image Matching Learning object identification rules for information integration On Automatic Question Answering Using Efficient Primal-Dual Models Text similarity matching method on basis of vector space model "Right Time, Right Place" Health Communication on Twitter: Value and Accuracy of Location Information Text-to-video: a semantic search engine for internet videos INTEGRATED ALERTING FOR STRUCTURED AND FREE-TEXT DATA IN TRIGGERMAN Modelling Interaction of Sentence Pair with Coupled-LSTMs aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model Explicit Interaction Model towards Text Classification A different image content-based retrievals using OCR techniques A new proof of the linearity of the Boyer-Moore string searching algorithm Analysis of Boyer-Moore-type string searching algorithms An ACL2 Proof of the Correctness of the Preprocessing for a Variant of the Boyer-Moore Fast String Searching Algorithm A reconfigurable array based prototype of a specialised string lookup chip Parameterized pattern matching by Boyer-Moore-type algorithms Siri Pembelajaran Bahasa Melayu Berasaskan Web Bagi Pelajar Sekolah Compact Approximation of Lattice Functions with Applications to Large-Alphabet Text Search Effective Duplicate Detection Using Generational Evolutionary Algorithm Matching Restaurant Menus to Crowdsourced Food Data: A Scalable Machine Learning Approach Product Recommendations Using Textual Similarity Based Learning Models Parallelization of KMP String Matching Algorithm on Different SIMD architectures: Multi-Core and GPGPU’s An approach to semantic-based model discovery and selection A scalable peer-to-peer system for music content and information retrieval Modulated string searching
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GREC ICDM Workshops Web Intelligence ICEEI DSA 2015 IEEE International Conference on Computer Vision (ICCV) EMNLP KEOD OTM Conferences (2) ECIR (2) ADHIP (1) DEXA Workshops ICDM Workshops EMNLP EMNLP CIKM SIGIR IC-AI MPRSS EMNLP CIKM AAAI FOCS SODA SODA KDD Winter Simulation Conference ISMIR
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