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Different Models and Approaches of Textual Entailment Recognition

Mohamed H., Marwa M.A., Ahmed Mohammed
2016 International Journal of Computer Applications  
The paper points to prominent testing data, training data, resources and Performance Evaluation for each model.  ...  This paper summarizes key ideas from the area of textual entailment recognition by considering in turn the different recognition models.  ...  entailment Chinese Textual Entailment Recognition This model presents a novel statistical method for recognizing Chinese textual entailment in MOD 4 Based on Syntactic Tree Clipping which lexical, syntactic  ... 
doi:10.5120/ijca2016909667 fatcat:cvjzyyidyjfmdnu2rjpgjao53y

Analysis of identifying linguistic phenomena for recognizing inference in text

Min-Yuh Day, Ya-Jung Wang
2014 Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)  
Although a considerable number of studies have been made on recognizing textual entailment, little is known about the power of linguistic phenomenon for recognizing inference in text.  ...  Recognizing Textual Entailment (RTE) is a task in which two text fragments are processed by system to determine whether the meaning of hypothesis is entailed from another text or not.  ...  [20] proposed an approach by leveraging diverse lexical resources for textual entailment recognition. Nguyen et al.  ... 
doi:10.1109/iri.2014.7051945 dblp:conf/iri/DayW14 fatcat:zkbrzbbalzg7ljx3jwmpafcule

Feature based Entailment Recognition for Malayalam Language Texts

Sara Renjit, Sumam Mary Idicula
2022 International Journal of Advanced Computer Science and Applications  
This work deals with various machine learning approaches applied to textual entailment recognition or natural language inference for Malayalam, a South Indian low resource language.  ...  Different lexical and surface-level features are used for this binary and multiclass classification.  ...  ACKNOWLEDGMENT The authors would like to thank the Department of Computer Science, CUSAT for the support extended in carrying out this research work.  ... 
doi:10.14569/ijacsa.2022.0130283 fatcat:3mugz2kpebepllwiryiyrouufm

Recognizing Textual Entailment Using Multiple Features

Yongmei Tan
2014 Journal of Information and Computational Science  
We evaluate our model on NTCIR-11 RITE dataset and then show how a combination of multiple features and filters can significantly improve the performance of recognizing textual entailmentover the best  ...  Textual entailment among sentences is an important part of applied semantic inference.  ...  We make the following contributions: 1) Different from traditional approaches, we present a novel framework for textual entailment recognition, which focus on multiple features and filters.  ... 
doi:10.12733/jics20102407 fatcat:ynzehm4iozh2tlwdt4c7kvshu4

A Survey of Paraphrasing and Textual Entailment Methods

I. Androutsopoulos, P. Malakasiotis
2010 The Journal of Artificial Intelligence Research  
We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.  ...  Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar.  ...  for document collections", which was co-funded by the European Union (80%) and the Greek General Secretariat for Research and Technology (20%).  ... 
doi:10.1613/jair.2985 fatcat:g6yso7ae7zeizbjbxp7efexoiu

Exploring lexical, syntactic, and semantic features for Chinese textual entailment in NTCIR RITE evaluation tasks

Wei-Jie Huang, Chao-Lin Liu
2015 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
We computed linguistic information at the lexical, syntactic, and semantic levels for Recognizing Inference in Text (RITE) tasks for both traditional and simplified Chinese in NTCIR-9 and NTCIR-10.  ...  Techniques for syntactic parsing, named-entity recognition, and near synonym recognition were employed, and features like counts of common words, statement lengths, negation words, and antonyms were considered  ...  In the 2012 Bakeoff for Chinese segmentation, the best performing system reached an F1 measure slightly shy of 95% (Duan, Sui, Tian, & Li 2012) Lexical semantics Lexical resources and computation  ... 
doi:10.1007/s00500-015-1629-1 fatcat:2jhermxnmzedtd5wyll7notjm4

Recognizing Textual Entailment [chapter]

Mark Sammons
2015 The Handbook of Contemporary Semantic Theory  
[50] define a focused textual entailment approach, SERR (Scalable Entailment Relation Recognition), that consists of two stages: semantic retrieval and entailment recognition.  ...  A number of open-source resources are used by multiple systems; rather than give multiple, repeated citations for each such resource, we simply name them here, and collect all this information at the end  ... 
doi:10.1002/9781118882139.ch17 fatcat:h5hpwxywa5gc7nyenwmsq3wlay

Validating Contradiction in Texts Using Online Co-Mention Pattern Checking

Chengwei Shih, Chengwei Lee, Richard Tzonghan Tsai, Wenlian Hsu
2012 ACM Transactions on Asian Language Information Processing  
Detecting contradictive statements is a foundational and challenging task for text understanding applications such as textual entailment.  ...  recognition.  ...  Our system achieved accuracy scores of 0.661 and 0.501 for traditional Chinese binary and multiple-class subtasks, respectively.  ... 
doi:10.1145/2382593.2382599 fatcat:6x6d34a3n5cx3hz7vdyr5z2plm

A survey on how to cross-reference web information sources

Joe Raad, Aurelie Bertaux, Christophe Cruz
2015 2015 Science and Information Conference (SAI)  
ACKNOWLEDGMENT We wish to thank the "Conseil Régional de Bourgogne" for its help in supporting and funding this work.  ...  In addition, we can see that both extraction and generation approaches are rarely used for paraphrase or textual entailment recognition and vice versa. C.  ...  We mention that: -TE: Textual Entailment; -P: Paraphrase; -Rec: Recognition; -Gen: Generation; -Ext: Extraction.  ... 
doi:10.1109/sai.2015.7237206 fatcat:syhjn5vvkzhptcvmd4wc34v3ue

Towards Logical Inference for Arabic Question-Answering

Wided Bakari, Patrice Bellot, Omar Trigui, Mahmoud Neji
2015 Research in Computing Science  
Now, our work is concentrated on an implementation step to develop a question-answering system in Arabic using the techniques of textual entailment recognition.  ...  The second one is the use of textual entailment techniques that relies on inference and logic representation to extract the candidate answer.  ...  Grau and his colleagues proposed an approach for selecting correct answers relies on textual entailment recognition between hypotheses and texts (Grau et al., 2012) .  ... 
doi:10.13053/rcs-90-1-7 fatcat:jnaexpzqrfftdj26t4vwkyzpqm

Textual Similarity Measurement Approaches: A Survey (1)

Amira Abo-Elghit, Aya Al-Zoghby, Taher Hamza
2020 The Egyptian Journal of Language Engineering  
However, many approaches for measuring textual similarity have been presented for Arabic text reviewed and compared in this paper.  ...  Finding the similarity between terms is the essential portion of textual similarity, then used as a major phase for sentence-level, paragraph-level, and script-level similarities.  ...  [41] addressed the problem of textual entailment in the Arabic Language.  ... 
doi:10.21608/ejle.2020.42018.1012 fatcat:a2fhtkub7nazlkgzqewqbb7koi

Computational semantic analysis of language: SemEval-2007 and beyond

Eneko Agirre, Lluís Màrquez, Richard Wicentowski
2009 Language Resources and Evaluation  
Also, we thank the authors of submitted papers for their interest and hard work.  ...  The Recognizing Textual Entailment challenge (RTE) has been run yearly since 2004. 10 This challenge proposes RTE as a generic task that captures major semantic inference needs across many natural language  ...  08 Metonymy Resolution at SemEval-2007 09 Multilevel Semantic Annotation of Catalan and Spanish 10 English Lexical Substitution Task 11 English Lexical Sample Task via English-Chinese Parallel  ... 
doi:10.1007/s10579-009-9091-2 fatcat:kcfwku252zg6pcrznoikzox6wq

Towards a Digital Infrastructure for Illustrated Handwritten Archives [chapter]

Andreas Weber, Mahya Ameryan, Katherine Wolstencroft, Lise Stork, Maarten Heerlien, Lambert Schomaker
2018 Lecture Notes in Computer Science  
By combining text and image recognition, we significantly transcend beyond the state-of-the art, and provide meaningful additions to integrated manuscript recognition.  ...  This separation extends to traditional handwriting recognition systems.  ...  Combining image and textual recognition into one digital infrastructure, allows for an integrated study of underexplored heritage collections and archives in general.  ... 
doi:10.1007/978-3-319-75826-8_13 fatcat:ytej62msh5b4xfmgyuh4srnq3a

Paraphrasing Chinese Idioms: Paraphrase Acquisition, Rewording and Scoring

Jia Jun, Dong Et.al
2021 Turkish Journal of Computer and Mathematics Education  
In this article, we describe our work in paraphrasing Chinese idioms by using the definitions from dictionaries.  ...  The definitions of the idioms will be reworded and then scored to find the best paraphrase candidates to be used for the given context.  ...  Acknowledgment The authors would like to express appreciation for the support of the Universiti Sains Malaysia [Project Number = 304.PKOMP.6316283].  ... 
doi:10.17762/turcomat.v12i3.1037 fatcat:3zxx3ylpovg4zcm4eb3a2nnaq4

Generating Phrasal and Sentential Paraphrases: A Survey of Data-Driven Methods

Nitin Madnani, Bonnie J. Dorr
2010 Computational Linguistics  
We also discuss the strategies used for evaluating paraphrase generation techniques and briefly explore some future trends in paraphrase generation.  ...  Moreover, the task of automatically generating or extracting semantic equivalences for the various units of languagewords, phrases, and sentences-is an important part of natural language processing (NLP  ...  We believe that such exploitation of multiple types of resources and their combinations is an important development.  ... 
doi:10.1162/coli_a_00002 fatcat:deov6ypw4bb7hfkkkvjxsyt6fy
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