A fundamental problem in text generation is word ordering . Word ordering is a computationally difficult problem , which can be constrained to some extent for particular applications , for example by using synchronous grammars for statistical machine translation . The results of these researches show that this characteristic comes from the mixture of both syntagmatic and foregrounded narrative type features in one text . Thus , it is of a considerable interest to analyse the subjective narrative level constructions , which presume to involve the reader in text generation process . Th e understanding of such text demands to fi nd a key or a code to its syntactic organization . To solve it we must separate the various relevant kinds of knowledge into nearly independent collections , factoring the problem . Inquiry semantics is a new factoring of the text generation problem . It is novel in that it provides a distinct semantics for the grammar , independent of world knowledge , discourse knowledge , text plans and the lexicon , but appropriately linked to each . The article points to description of some methods of teaching students textual activity including games with a word , creating texts , which show a content of linguistic terms ; modelling of speech situations . The study shows advantages of these methods in teaching students to text generating . Both quantitative and qualitative analyses were performed to complement the possible inadequacy of either analytical method and for the purpose of data triangulation . The findings show that the intervention achieved a significant treatment effect on both the participants ' writing competence and their strategy use , namely , text-generating , feedback handling , and revising . The qualitative analyses also suggest the experimental students orchestrated their strategy use better than before . Deep Learning to the Rescue!. Based on recent advances in natural language modeling and those in text generation capabilities , we propose a novel data augmentation method for text classification tasks . We use a powerful pre-trained neural network model to artificially synthesize new labeled data for supervised learning . The fuzzy descriptors are meant to be used as the cornerstones of a geographical referring expression generation algorithm that is able to linguistically characterize geographical locations and regions . This work is also part of a general research effort that intends to establish a methodology which reunites the empirical studies traditionally practiced in data-to-text and the use of fuzzy sets to model imprecision and vagueness in words and expressions for text generation purposes . Embodiments of the invention disclose a message playing method , a system and related equipment thereof which are applied in the communication technology field . In the embodiments of the invention , a receiving terminal receives a message comprising a user identification and text information , acquires a voice identification and image identification corresponding to the user identification , acquires a voice characteristic parameter which is indicated according to the voice identification , an image characteristic parameter which is indicated according to the image identification and text information generation or acquires a voice animation flow , and plays the voice animation flow . The text information in the message can be played into the voice animation flow according to the user identification . The Monte Carlo approach is used to generate synthetic data . The running time of TextGen generation depends only on the expected data size , which means that the time complexity of TextGen is O(n ) . To evaluate TextGen , four real-world datasets were used to perform an experiment . In fact , the results they yielded have been so good that the gap between human translations and machine translations has narrowed significantly and LSTM - based recurrent neural networks have become standard in natural language processing . Even more recently , auto - regressive convolutional models have proven highly effective when applied to audio wavenet2016 , image pixelcnn2016 and text generation bytenet2016 . Their success on sequence data in particular rivals or surpasses that of previous recurrent models bytenet2016 , fbpaper . section : Related Work Generating text that mimics human 's expression has been studied for poem generation [ reference ] , image captioning , dialogue system ) machine translation . [ reference ] proposed a recurent neural network ( RNN ) based generative model to use the human - generated text where at each step the model tries to predict the next word given previous real word sequence and is trained in a supervised fashion . REINFORCE side steps the issues associated with the discrete nature of the optimization by not requiring rewards ( or losses ) to be differentiable . While REINFORCE appears to be well suited to tackle the text generation problem , it suffers from a significant issue . The problem setting of text generation has a very large action space which makes it extremely difficult to learn with an initial random policy . subsection : Text Generation For text generation scenarios , we apply the proposed SeqGAN to generate Chinese poems and Barack Obama political speeches . In the poem composition task , we use a corpus of 16 , 394 Chinese quatrains , each containing four lines of twenty characters in total . In this work , we leverage the merits from accurate region proposal based methods , and flexible segmentation based methods which can easily generate arbitrary - shaped text mask . It is an end - to - end trainable framework excluding redundant and low - efficient pipelines such as the use of text / nontext salient map and text - line generation . Based on region proposal network ( RPN ) , our approach detects and segments text instance simultaneously , followed by non - maximum suppression ( NMS ) to suppress overlapping instances . Multi-document summarization is the automatic produ ction of a unique summary from a collection of texts . In this paper , we propose a statistical generative approach for multi-document summarization that combines simple information such as sentence positi on in the text and semantic-discursive information from CST ( Cross-Doc ument Structure Theory ) . In particular , we formulate the multi-docu ment summarization task using a Noisy-Channel model . The JTMMR model can improve the effect of the manifold ranking method by using the better semantic feature . Experiments show that our models can achieve a better result than other baseline models for multi-document summarization task . At the same time , our models also have a good performance on the single document summarization task . Multi-document automatic summarization is an important branch of natural language understanding . But the methods of evaluation of the Multi-document automatic summarization also have many problems , which are single and lack of uniform standard . The aim of multi-document automatic summarization is to provide people a concise and full-information document and help people get information effectively . When partial topic is identified , clustering method is usually used to cluster similar document units into a partial topic . Multi-document summarization poses quite significant challenges like summary generation , evaluation , compression , speed , etc . This paper addresses mainly the issue of merging two or more similar documents or summaries for multi-document text summarization . Important sentences extracted from multiple-related sources are merged to form a consolidated summary thereby producing coherent and non-repetitive summaries . This paper presents , a comparison between Extrinsic and Intrinsic technique for multi-document text summarization has been presented . In extrinsic technique , lexical similarity measure and in intrinsic technique , semantic similarity measure between the sentences has been evaluated . Related work sections or literature reviews are an essential part of every scientific article being crucial for paper reviewing and assessment . The automatic generation of related work sections can be considered an instance of the multi-document summarization problem . In order to allow the study of this specific problem , we have developed a manually annotated , machine readable data-set of related work sections , cited papers ( e.g. references ) and sentences , together with an additional layer of papers citing the references . This paper proposes a novel method using K-means and Non-negative matrix factorization ( NMF ) for topic -based multi-document summarization . NMF decomposes weighted term by sentence matrix into two sparse non-negative matrices : semantic feature matrix and semantic variable matrix . This method combines the use of a semantic graph for representing textual contents and identify the most relevant topics with the processing of several sentences features applying a fuzzy logic perspective . A fuzzy aggregation operator is applied in the sentences relevance assessment process as a contribution to the multi-document summarization process . The method was evaluated with the Spanish and English texts collection of MultiLing 2015 . Well Recognized Transcripts.- Short Papers.- Natural Language Processing for Usage Based Indexing of Web Resources.- Harnessing Trust in Social Search.- How to Compare Bilingual to Monolingual Cross-Language Information Retrieval.- Multilingual Text Classification Using Ontologies.- Using Visual-Textual Mutual Information and Entropy for Inter-modal Document Indexing.- A Study of Global Inference Algorithms in Multi-document Summarization.- Document Representation Using Global Association Distance Model.- Sentence Level Sentiment Analysis in the Presence of Conjuncts Using Linguistic Analysis.- PageRank : When Order Changes.- Model Tree Learning for Query Term Weighting in Question Answering.- Examining Repetition in User Search Behavior.- Popularity Weighted Ranking for Academic Digital Libraries.- Naming Functions for the Vector Space Model.- Effective Use of Semantic Structure in XML Retrieval.- Searching Documents Based on Relevance and Type.- Investigation of the Effectiveness of Cross-Media Indexing.- Improve Ranking by Using Image Information.- N-Step PageRank for Web Search.- Authorship Attribution Via Combination of Evidence.- Posters.- Cross-Document Entity Tracking.- Enterprise People and Skill Discovery Using Tolerant Retrieval and Visualization.- Experimental Results of the Signal Processing Approach to Distributional Clustering of Terms on Reuters-21578 Collection.- Overall Comparison at the Standard Levels of Recall of Multiple Retrieval Methods with the Friedman Test.- Building a Desktop Search Test-Bed.- Hierarchical Browsing of Video Key Frames.- Active Learning with History-Based Query Selection for Text Categorisation.- Fighting Link Spam with a Two-Stage Ranking Strategy.- Improving Naive Bayes Text Classifier Using Smoothing Methods.- Term Selection and Query Operations for Video Retrieval.- An Effective Threshold-Based Neighbor Selection in Collaborative Filtering.- Combining Multiple Sources of Evidence in XML Multimedia Documents : An Inference Network Incorporating Element Language Models.- Language Model Based Query Classification.- Integration of Text and Audio Features for Genre Classification in Music Information Retrieval.- Retrieval Method for Video Content in Different Format Based on Spatiotemporal Features.- Combination of Document Priors in Web Information Retrieval.- Enhancing Expert Search Through Query The following is a brief description of the features we used in the actual submissions , which gave us the best results on the training data : • Centroid : The centroid score that is a measure of how close is the sentence to the centroid pseudosentence of the entire cluster . This is a measure of sentence salience which is proven to be successful in multi-document summarization domain ( Radev , Jing , & Budzikowska , 2000 ) . • LexRank : We also note that this work is an extended version of nallapati . In addition to performing more extensive experiments compared to that work , we also propose a novel dataset for document summarization on which we establish benchmark numbers too . Below , we analyze the similarities and differences of our proposed models with related work on summarization . Each of our proposed novel models addresses a specific problem in abstractive summarization , yielding further improvement in performance . We also propose a new dataset for multi - sentence summarization and establish benchmark numbers on it . As part of our future work , we plan to focus our efforts on this data and build more robust models for summaries consisting of multiple sentences . Other work attempts to bypass basic extraction tasks and address more complex biomedical QA and ( multi - document ) summarization problems to support EBM . Such systems would directly benefit from more accurate extraction of the types codified in the corpus we present here . Identifying similar text passages plays an important role in many applications in NLP , such as paraphrase generation , automatic summarization , etc . This paper presents some experiments on detecting and clustering similar sentences of texts in Brazilian Portuguese . To date most studies on corpus extension focused on paraphrasing and resorted to crowd-sourcing in order to obtain paraphrases , which then required additional manual validation often performed by system developers . We prove that automatic corpus extension by means of paraphrase extraction and validation is just as effective as crowd-sourcing , being at the same time less costly in terms of development time and resources . During intermediate experiments our generation models showed a significantly better performance than the phrase-based baseline model and appeared to be more robust in handling unknown combinations of concepts than the current in-house rule-based generator . The integrated predicate nouns co-occur with the support verb fazer ( do or make ) and their syntactic and distributional properties are formalized in lexicon-grammar tables . These lexicon-grammar tables resulted in a standalone dictionary of predicate noun constructions and a few new grammars that can be used in paraphrase analysis and generation . This corpus will be a valuable evaluation resource for computational linguists investigating similarity and relatedness in short texts . Furthermore , this dataset can be used for evaluation of paraphrase analysis and machine translation tasks . Our dataset is characterised by : ( 1 ) superior quality of relatedness assignment ; as we have incorporated relations marked by well-known domain experts , this dataset could thus be considered a gold standard corpus for various evaluation tasks , ( 2 ) the size of our dataset ; over 7,600 pairs of related verses are collected from scholarly sources with several levels of degree of relatedness . In this paper , we present PREFER , a paraphrase reference tool for helping language learners improve their writing skills . In this paper , we attempt to transform the paraphrase generation problem into a graphical problem in which the phrases are treated as nodes and translation similarities as edges . We adopt the PageRank algorithm to rank and filter the paraphrases generated by the pivot-based paraphrase generation method . Creating Paraphrase Identification Corpus for Indian Languages : Opensource Data Set for Paraphrase Creation To generate candidates and rank them in context , we have used the PPDB and WordNet paraphrase resources . We use the Concepts in Context ( CoInCO ) " All-Words " lexical substitution dataset both for the informal word identification and paraphrase generation experiments . Our informal word identification component achieves an F-1 score of 82 % , significantly outperforming a stratified classifier baseline . To overcome these barriers , an emerging Hierarchical Conditional Attention-based Neural Networks ( HCANN ) architecture to construct end-to-end text generation framework is proposed . More specifically , included method in that represents hierarchy of document along with conditional decoder for paraphrase generation processes . Quantitative evaluation of the method on several benchmark paraphrase datasets demonstrates its efficiency and performance capability by a significant margin . Paraphrase generation is an interesting and challenging NLP task which has numerous practical applications . In this paper , we analyze datasets commonly used for paraphrase generation research , and show that simply parroting input sentences surpasses state-of-the-art models in the literature when evaluated on standard metrics . Our findings illustrate that a model could be seemingly adept at generating paraphrases , despite only making trivial changes to the input sentence or even none at all . In the experiments on improved AMPBM , we evaluate our method and compare the resulting PPDB with four types of databases ; PPDB constructed by using the original AMBPM , two PPDBs constructed by using two types of word-embedding-based methods ( stem embedding and phrase embedding ) , and an existing thesaurus . The comparison is performed by using two NLP applications : sentential paraphrase generation and a question answering ( QA ) system . The experimental results demonstrate that , AMBPM outperforms the state-of-the-art paraphrase extraction methods . However , these prior works have only utilized limited syntactic information available in the parse tree of the exemplar sentence . We address this limitation in the paper and propose Syntax Guided Controlled Paraphraser ( SGCP ) , an end-to-end framework for syntactic paraphrase generation . We find that Sgcp can generate syntax-conforming sentences while not compromising on relevance . Arc - I and SENNA + MLP come next . subsection : Experiment III : Paraphrase Identification Paraphrase identification aims to determine whether two sentences have the same meaning , a problem considered a touchstone of natural language understanding . subsection : Experiment on Quora Question Pair dataset In this subsection , we evaluate the effectiveness of our model for paraphrase identification as natural language inference task . Other than our baselines , we compare with and . During investigation , we hypothesize exact match feature helps the model to better understand paraphrase , therefore we study the result from second ablation ablation study where exact match feature is not used . Surprisingly , the model without exact model feature does not work worse on PARAPHRASE , instead , the accuracy on ANTO drops about 10 % . DIIN is also work well on LONG SENTENCE , partially because the receptive field is large enough to cover all tokens . It is also notable that our proposed architecture does not restrict the selection of pooling method ; the performance could further be improved by replacing max - pooling with other advanced algorithms e.g. intra - sentence attention and generalized pooling . subsection : Paraphrase Identification We use Quora Question Pairs dataset in evaluating the performance of our method on the PI task . These tasks evaluate how the cosine distance between two sentences correlate with a human - labeled similarity score through Pearson and Spearman correlations . paragraph : Paraphrase detection The Microsoft Research Paraphrase Corpus is composed of pairs of sentences which have been extracted from news sources on the Web . We randomly select 50 incorrect questions ( based on EM ) and categorize them into 6 classes . 50 % of errors are due to the imprecise boundaries of the answers , 28 % involve syntactic complications and ambiguities , 14 % are paraphrase problems , 4 % require external knowledge , 2 % need multiple sentences to answer , and 2 % are due to mistakes during tokenization . See Appendix [ reference ] for the examples of the error modes . In short , Story Completion can improve students ’ Vocabulary Mastery . Keywords : story completion , improving , vocabulary mastery Two deep learning techniques namely Long Short Term Memory ( LSTM ) and Bi-LSTM have been explored for the task of predicting next word and accuracy of 59.46 % and 81.07 % was observed for LSTM and Bi-LSTM respectively . This approach may be used for various NLG tasks like story auto-completion , sentence autocompletion , etc . However , speakers under load may also have difficulty taking into account their addressee ’s perspective , which may either lead to more use of the speaker ’s own discourse model or to more economic expressions . To tease these effects apart , we conducted a story completion experiment in which cognitive load was manipulated by the presence or absence of a secondary task for the speaker . In addition , we dissociated the speaker ’s and the addressee ’s perspectives . Learning action is to improve fraction grasp concept with original object media . Data of capability improvement of story problem finishing is collected techniques of this research are observation , test and documentation . The data was analyzed by using an interactive analysis model with three components ; data reduction , data presentation and conclusion or verification .
A fundamental problem in text generation is word ordering . Word ordering is a computationally difficult problem , which can be constrained to some extent for particular applications , for example by using synchronous grammars for statistical machine translation .
The results of these researches show that this characteristic comes from the mixture of both syntagmatic and foregrounded narrative type features in one text . Thus , it is of a considerable interest to analyse the subjective narrative level constructions , which presume to involve the reader in text generation process . Th e understanding of such text demands to fi nd a key or a code to its syntactic organization .
To solve it we must separate the various relevant kinds of knowledge into nearly independent collections , factoring the problem . Inquiry semantics is a new factoring of the text generation problem . It is novel in that it provides a distinct semantics for the grammar , independent of world knowledge , discourse knowledge , text plans and the lexicon , but appropriately linked to each .
The article points to description of some methods of teaching students textual activity including games with a word , creating texts , which show a content of linguistic terms ; modelling of speech situations . The study shows advantages of these methods in teaching students to text generating .
Both quantitative and qualitative analyses were performed to complement the possible inadequacy of either analytical method and for the purpose of data triangulation . The findings show that the intervention achieved a significant treatment effect on both the participants ' writing competence and their strategy use , namely , text-generating , feedback handling , and revising . The qualitative analyses also suggest the experimental students orchestrated their strategy use better than before .
Deep Learning to the Rescue!. Based on recent advances in natural language modeling and those in text generation capabilities , we propose a novel data augmentation method for text classification tasks . We use a powerful pre-trained neural network model to artificially synthesize new labeled data for supervised learning .
The fuzzy descriptors are meant to be used as the cornerstones of a geographical referring expression generation algorithm that is able to linguistically characterize geographical locations and regions . This work is also part of a general research effort that intends to establish a methodology which reunites the empirical studies traditionally practiced in data-to-text and the use of fuzzy sets to model imprecision and vagueness in words and expressions for text generation purposes .
Embodiments of the invention disclose a message playing method , a system and related equipment thereof which are applied in the communication technology field . In the embodiments of the invention , a receiving terminal receives a message comprising a user identification and text information , acquires a voice identification and image identification corresponding to the user identification , acquires a voice characteristic parameter which is indicated according to the voice identification , an image characteristic parameter which is indicated according to the image identification and text information generation or acquires a voice animation flow , and plays the voice animation flow . The text information in the message can be played into the voice animation flow according to the user identification .
The Monte Carlo approach is used to generate synthetic data . The running time of TextGen generation depends only on the expected data size , which means that the time complexity of TextGen is O(n ) . To evaluate TextGen , four real-world datasets were used to perform an experiment .
In fact , the results they yielded have been so good that the gap between human translations and machine translations has narrowed significantly and LSTM - based recurrent neural networks have become standard in natural language processing . Even more recently , auto - regressive convolutional models have proven highly effective when applied to audio wavenet2016 , image pixelcnn2016 and text generation bytenet2016 . Their success on sequence data in particular rivals or surpasses that of previous recurrent models bytenet2016 , fbpaper .
section : Related Work Generating text that mimics human 's expression has been studied for poem generation [ reference ] , image captioning , dialogue system ) machine translation . [ reference ] proposed a recurent neural network ( RNN ) based generative model to use the human - generated text where at each step the model tries to predict the next word given previous real word sequence and is trained in a supervised fashion .
REINFORCE side steps the issues associated with the discrete nature of the optimization by not requiring rewards ( or losses ) to be differentiable . While REINFORCE appears to be well suited to tackle the text generation problem , it suffers from a significant issue . The problem setting of text generation has a very large action space which makes it extremely difficult to learn with an initial random policy .
subsection : Text Generation For text generation scenarios , we apply the proposed SeqGAN to generate Chinese poems and Barack Obama political speeches . In the poem composition task , we use a corpus of 16 , 394 Chinese quatrains , each containing four lines of twenty characters in total .
In this work , we leverage the merits from accurate region proposal based methods , and flexible segmentation based methods which can easily generate arbitrary - shaped text mask . It is an end - to - end trainable framework excluding redundant and low - efficient pipelines such as the use of text / nontext salient map and text - line generation . Based on region proposal network ( RPN ) , our approach detects and segments text instance simultaneously , followed by non - maximum suppression ( NMS ) to suppress overlapping instances .
Multi-document summarization is the automatic produ ction of a unique summary from a collection of texts . In this paper , we propose a statistical generative approach for multi-document summarization that combines simple information such as sentence positi on in the text and semantic-discursive information from CST ( Cross-Doc ument Structure Theory ) . In particular , we formulate the multi-docu ment summarization task using a Noisy-Channel model .
The JTMMR model can improve the effect of the manifold ranking method by using the better semantic feature . Experiments show that our models can achieve a better result than other baseline models for multi-document summarization task . At the same time , our models also have a good performance on the single document summarization task .
Multi-document automatic summarization is an important branch of natural language understanding . But the methods of evaluation of the Multi-document automatic summarization also have many problems , which are single and lack of uniform standard .
The aim of multi-document automatic summarization is to provide people a concise and full-information document and help people get information effectively . When partial topic is identified , clustering method is usually used to cluster similar document units into a partial topic .
Multi-document summarization poses quite significant challenges like summary generation , evaluation , compression , speed , etc . This paper addresses mainly the issue of merging two or more similar documents or summaries for multi-document text summarization . Important sentences extracted from multiple-related sources are merged to form a consolidated summary thereby producing coherent and non-repetitive summaries .
This paper presents , a comparison between Extrinsic and Intrinsic technique for multi-document text summarization has been presented . In extrinsic technique , lexical similarity measure and in intrinsic technique , semantic similarity measure between the sentences has been evaluated .
Related work sections or literature reviews are an essential part of every scientific article being crucial for paper reviewing and assessment . The automatic generation of related work sections can be considered an instance of the multi-document summarization problem . In order to allow the study of this specific problem , we have developed a manually annotated , machine readable data-set of related work sections , cited papers ( e.g. references ) and sentences , together with an additional layer of papers citing the references .
This paper proposes a novel method using K-means and Non-negative matrix factorization ( NMF ) for topic -based multi-document summarization . NMF decomposes weighted term by sentence matrix into two sparse non-negative matrices : semantic feature matrix and semantic variable matrix .
This method combines the use of a semantic graph for representing textual contents and identify the most relevant topics with the processing of several sentences features applying a fuzzy logic perspective . A fuzzy aggregation operator is applied in the sentences relevance assessment process as a contribution to the multi-document summarization process . The method was evaluated with the Spanish and English texts collection of MultiLing 2015 .
Well Recognized Transcripts.- Short Papers.- Natural Language Processing for Usage Based Indexing of Web Resources.- Harnessing Trust in Social Search.- How to Compare Bilingual to Monolingual Cross-Language Information Retrieval.- Multilingual Text Classification Using Ontologies.- Using Visual-Textual Mutual Information and Entropy for Inter-modal Document Indexing.- A Study of Global Inference Algorithms in Multi-document Summarization.- Document Representation Using Global Association Distance Model.- Sentence Level Sentiment Analysis in the Presence of Conjuncts Using Linguistic Analysis.- PageRank : When Order Changes.- Model Tree Learning for Query Term Weighting in Question Answering.- Examining Repetition in User Search Behavior.- Popularity Weighted Ranking for Academic Digital Libraries.- Naming Functions for the Vector Space Model.- Effective Use of Semantic Structure in XML Retrieval.- Searching Documents Based on Relevance and Type.- Investigation of the Effectiveness of Cross-Media Indexing.- Improve Ranking by Using Image Information.- N-Step PageRank for Web Search.- Authorship Attribution Via Combination of Evidence.- Posters.- Cross-Document Entity Tracking.- Enterprise People and Skill Discovery Using Tolerant Retrieval and Visualization.- Experimental Results of the Signal Processing Approach to Distributional Clustering of Terms on Reuters-21578 Collection.- Overall Comparison at the Standard Levels of Recall of Multiple Retrieval Methods with the Friedman Test.- Building a Desktop Search Test-Bed.- Hierarchical Browsing of Video Key Frames.- Active Learning with History-Based Query Selection for Text Categorisation.- Fighting Link Spam with a Two-Stage Ranking Strategy.- Improving Naive Bayes Text Classifier Using Smoothing Methods.- Term Selection and Query Operations for Video Retrieval.- An Effective Threshold-Based Neighbor Selection in Collaborative Filtering.- Combining Multiple Sources of Evidence in XML Multimedia Documents : An Inference Network Incorporating Element Language Models.- Language Model Based Query Classification.- Integration of Text and Audio Features for Genre Classification in Music Information Retrieval.- Retrieval Method for Video Content in Different Format Based on Spatiotemporal Features.- Combination of Document Priors in Web Information Retrieval.- Enhancing Expert Search Through Query
The following is a brief description of the features we used in the actual submissions , which gave us the best results on the training data : • Centroid : The centroid score that is a measure of how close is the sentence to the centroid pseudosentence of the entire cluster . This is a measure of sentence salience which is proven to be successful in multi-document summarization domain ( Radev , Jing , & Budzikowska , 2000 ) . • LexRank :
We also note that this work is an extended version of nallapati . In addition to performing more extensive experiments compared to that work , we also propose a novel dataset for document summarization on which we establish benchmark numbers too . Below , we analyze the similarities and differences of our proposed models with related work on summarization .
Each of our proposed novel models addresses a specific problem in abstractive summarization , yielding further improvement in performance . We also propose a new dataset for multi - sentence summarization and establish benchmark numbers on it . As part of our future work , we plan to focus our efforts on this data and build more robust models for summaries consisting of multiple sentences .
Other work attempts to bypass basic extraction tasks and address more complex biomedical QA and ( multi - document ) summarization problems to support EBM . Such systems would directly benefit from more accurate extraction of the types codified in the corpus we present here .
Identifying similar text passages plays an important role in many applications in NLP , such as paraphrase generation , automatic summarization , etc . This paper presents some experiments on detecting and clustering similar sentences of texts in Brazilian Portuguese .
To date most studies on corpus extension focused on paraphrasing and resorted to crowd-sourcing in order to obtain paraphrases , which then required additional manual validation often performed by system developers . We prove that automatic corpus extension by means of paraphrase extraction and validation is just as effective as crowd-sourcing , being at the same time less costly in terms of development time and resources . During intermediate experiments our generation models showed a significantly better performance than the phrase-based baseline model and appeared to be more robust in handling unknown combinations of concepts than the current in-house rule-based generator .
The integrated predicate nouns co-occur with the support verb fazer ( do or make ) and their syntactic and distributional properties are formalized in lexicon-grammar tables . These lexicon-grammar tables resulted in a standalone dictionary of predicate noun constructions and a few new grammars that can be used in paraphrase analysis and generation .
This corpus will be a valuable evaluation resource for computational linguists investigating similarity and relatedness in short texts . Furthermore , this dataset can be used for evaluation of paraphrase analysis and machine translation tasks . Our dataset is characterised by : ( 1 ) superior quality of relatedness assignment ; as we have incorporated relations marked by well-known domain experts , this dataset could thus be considered a gold standard corpus for various evaluation tasks , ( 2 ) the size of our dataset ; over 7,600 pairs of related verses are collected from scholarly sources with several levels of degree of relatedness .
In this paper , we present PREFER , a paraphrase reference tool for helping language learners improve their writing skills . In this paper , we attempt to transform the paraphrase generation problem into a graphical problem in which the phrases are treated as nodes and translation similarities as edges . We adopt the PageRank algorithm to rank and filter the paraphrases generated by the pivot-based paraphrase generation method .
Creating Paraphrase Identification Corpus for Indian Languages : Opensource Data Set for Paraphrase Creation
To generate candidates and rank them in context , we have used the PPDB and WordNet paraphrase resources . We use the Concepts in Context ( CoInCO ) " All-Words " lexical substitution dataset both for the informal word identification and paraphrase generation experiments . Our informal word identification component achieves an F-1 score of 82 % , significantly outperforming a stratified classifier baseline .
To overcome these barriers , an emerging Hierarchical Conditional Attention-based Neural Networks ( HCANN ) architecture to construct end-to-end text generation framework is proposed . More specifically , included method in that represents hierarchy of document along with conditional decoder for paraphrase generation processes . Quantitative evaluation of the method on several benchmark paraphrase datasets demonstrates its efficiency and performance capability by a significant margin .
Paraphrase generation is an interesting and challenging NLP task which has numerous practical applications . In this paper , we analyze datasets commonly used for paraphrase generation research , and show that simply parroting input sentences surpasses state-of-the-art models in the literature when evaluated on standard metrics . Our findings illustrate that a model could be seemingly adept at generating paraphrases , despite only making trivial changes to the input sentence or even none at all .
In the experiments on improved AMPBM , we evaluate our method and compare the resulting PPDB with four types of databases ; PPDB constructed by using the original AMBPM , two PPDBs constructed by using two types of word-embedding-based methods ( stem embedding and phrase embedding ) , and an existing thesaurus . The comparison is performed by using two NLP applications : sentential paraphrase generation and a question answering ( QA ) system . The experimental results demonstrate that , AMBPM outperforms the state-of-the-art paraphrase extraction methods .
However , these prior works have only utilized limited syntactic information available in the parse tree of the exemplar sentence . We address this limitation in the paper and propose Syntax Guided Controlled Paraphraser ( SGCP ) , an end-to-end framework for syntactic paraphrase generation . We find that Sgcp can generate syntax-conforming sentences while not compromising on relevance .
Arc - I and SENNA + MLP come next . subsection : Experiment III : Paraphrase Identification Paraphrase identification aims to determine whether two sentences have the same meaning , a problem considered a touchstone of natural language understanding .
subsection : Experiment on Quora Question Pair dataset In this subsection , we evaluate the effectiveness of our model for paraphrase identification as natural language inference task . Other than our baselines , we compare with and .
During investigation , we hypothesize exact match feature helps the model to better understand paraphrase , therefore we study the result from second ablation ablation study where exact match feature is not used . Surprisingly , the model without exact model feature does not work worse on PARAPHRASE , instead , the accuracy on ANTO drops about 10 % . DIIN is also work well on LONG SENTENCE , partially because the receptive field is large enough to cover all tokens .
It is also notable that our proposed architecture does not restrict the selection of pooling method ; the performance could further be improved by replacing max - pooling with other advanced algorithms e.g. intra - sentence attention and generalized pooling . subsection : Paraphrase Identification We use Quora Question Pairs dataset in evaluating the performance of our method on the PI task .
These tasks evaluate how the cosine distance between two sentences correlate with a human - labeled similarity score through Pearson and Spearman correlations . paragraph : Paraphrase detection The Microsoft Research Paraphrase Corpus is composed of pairs of sentences which have been extracted from news sources on the Web .
We randomly select 50 incorrect questions ( based on EM ) and categorize them into 6 classes . 50 % of errors are due to the imprecise boundaries of the answers , 28 % involve syntactic complications and ambiguities , 14 % are paraphrase problems , 4 % require external knowledge , 2 % need multiple sentences to answer , and 2 % are due to mistakes during tokenization . See Appendix [ reference ] for the examples of the error modes .
In short , Story Completion can improve students ’ Vocabulary Mastery . Keywords : story completion , improving , vocabulary mastery
Two deep learning techniques namely Long Short Term Memory ( LSTM ) and Bi-LSTM have been explored for the task of predicting next word and accuracy of 59.46 % and 81.07 % was observed for LSTM and Bi-LSTM respectively . This approach may be used for various NLG tasks like story auto-completion , sentence autocompletion , etc .
However , speakers under load may also have difficulty taking into account their addressee ’s perspective , which may either lead to more use of the speaker ’s own discourse model or to more economic expressions . To tease these effects apart , we conducted a story completion experiment in which cognitive load was manipulated by the presence or absence of a secondary task for the speaker . In addition , we dissociated the speaker ’s and the addressee ’s perspectives .
Learning action is to improve fraction grasp concept with original object media . Data of capability improvement of story problem finishing is collected techniques of this research are observation , test and documentation . The data was analyzed by using an interactive analysis model with three components ; data reduction , data presentation and conclusion or verification .
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EACL EACL FUZZ-IEEE ICLR AAAI ICLR AAAI 2018 24th International Conference on Pattern Recognition (ICPR) STIL LREC SOCO TREC CoNLL ACL PROPOR NooJ LREC BEA@NAACL-HLT MIWAI NIPS ICLR ACML EMNLP ICLR ICDSE CogSci
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