Research on Recognition Method of Textual Implication
Pages 25 - 31
Abstract
Text implication recognition aims to judge the semantic and logical relationship between two paragraphs of text. The reasoning process involves syntactic analysis, vocabulary understanding, logical reasoning, social experience, and common sense. It is a way to judge whether the computer is to a certain extent. The challenging research task of "understanding" text semantics is also one of the more important benchmark tasks in the field of natural language processing. It is found that in the past ten years, the total number of publications in this field has been increasing year by year, and the popularity is also increasing. At present, there are mainly text implication relationship recognition methods based on similarity, text implication relationship recognition methods based on alignment, and deep neural networks. The textual implication relationship recognition method. This article summarizes the different methods.
References
[1]
Guo Maosheng, Zhang Yu, Liu Ting. Research progress and prospects of textual implication recognition and knowledge acquisition[J]. Chinese Journal of Computers, 2017, 40(4):889-910.
[2]
Kratzwald, B., Ilić, S., Kraus, M., Feuerriegel, S., & Prendinger, H. (2018). Deep learning for affective computing: Textbased emotion recognition in decision support. Decision Support Systems, 115, 24-35.
[3]
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25.
[4]
Almuhanna, A. A., Yafooz, W., & Alsaeedi, A. (2022). An Interactive Scholarly Collaborative Network Based on Academic Relationships and Research Collaborations. Applied Sciences, 12(2), 915.
[5]
Zhang Z, Yao D, Pang . Chinese Textual Entailment Recognition Enhanced with Word Embedding[C/OL]// Sun M, Liu Z, Zhang M, Lecture Notes in Computer Science, Vol 9427:Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data 14th China National Conference, CCL 2015 and Third International Symposium, NLP-NABD 2015,Guangzhou, China, November 13-14, 2015, Proceedings. Springer, 2015:89-100. hops://doi.org/10.1007/978-3-319-25816-4-8.
[6]
Sheng Yaqi; Zhang Han; Lu Chen; Ji Donghong. Text implication recognition based on mixed topic model "J]. Computer Engineering, 2015 (05): 180-184.
[7]
Zhao Hongyan; Liu Peng; Li Ru; Wang Zhiqiang. Research on multi-feature text implication recognitionJ]. Chinese Journal of Information, 2014(02):109-115.
[8]
Jijkoun V, de Rijke M, others. Recognizing textual entailment using lexical similarity[C]// Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment. 2005:73-76.
[9]
Lin D. Extracting Collocations from Text Corpora[C]// First Workshop on Computational Terminology. Citeseer, 1998:57-63.
[10]
Miller G A. WordNet: A Lexical Database for English[J/OL]. Commun. ACM 1995, 38(11):39-41.http://doi.acm.org/10.1145/219717.219748.
[11]
Adams R, Nicolae G, Nicolae C, Textual Entailment Through Extended Lexical Overlap and Lexico-Semantic Matching[C/OL]// Sekine S, Inui K, Dagan I, Proceedings of the ACL-PASCAL@ACL 2007 Workshop on Textual Entailment and Paraphrasing, Prague, Czech Republic, June 28-29, 2007. Association for Computational Linguistics, 2007:119-124. hops://aclanthology.info/papers/W07-1420/w07-1420.
[12]
Zhang Peng; Li Guochen; Li Ru; Liu Haijing; Shi Xiangrong. Textual implication recognition based on FrameNet framework relations [J]. Journal of Chinese Information Processing, 2012(02): 46-50. Baker C F, Fillmore C J, Lowe J B. The Berkeley FrameNet Project[C/OL] // Boitet C, Whitelock P. 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, COLING-ACL '98, August 10-14, 1998, Universite de Montreal, Montreal, Quebec, Canada. Proceedings of the Conference Morgan Kaufmann Publishers/ACL, 1998:86-90. http://aclweb.org/anthology/P/P98/P98-1013.pdf.
[13]
Mehdad Y, Negri M, Cabrio E, EDITS: An Open Source Framework for Recognizing Textual Entailment[C]// Text Analysis Conference (TAC 2009). 2009.1613/jair.2985.
[14]
Heilman M, Smith N A. Tree Edit Models for Recognizing Textual Entailments,Paraphrases, and Answers to Questions[C/OL]// Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, June 2-4, 2010, Los Angeles, California, USA. [5.1.]:The Association for Computational Linguistics, 2010:1011一1019. http://www.aclweb.org/anthology/N 10-1145.
[15]
de Marneffe M, Rafferty A N, Manning C D. Finding Contradictions in Text[C/OL]// McKeown K R, Moore J D, Teufel S, ACL 2008, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, June 15-20,2008, Columbus, Ohio, USA. [5.1.]:The Association for Computer Linguistics, 2008:1039-1047. http://www.aclweb.org/anthology/P08- 1118.
[16]
Iftene A. UAIC Participation at RTE4[C/OL]// Proceedings of the First Text Analysis Conference, TAC 2008, Gaithersburg, Maryland, USA, November 17-19, 2008. NIST, http://www.nist.gov/tac/publications/2008/participant.papers/UAIC2008.proceedings.pdf.
[17]
Lin D, Pantel P. DIRT @SBT@discovery of inference rules from text[C/OL]// Lee D, Schkolnick M, Provost F J, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, San Francisco, CA, USA, August 26-29, 2001. ACM, 2001:323-328. http://portal.acm.org/citation.cfm?id=502512.502559.
[18]
Chklovski T, Pantel P. VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations[C/OL]// Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, EMNLP 2004, A meeting of SIGDAT, a Special Interest Group of the ACL, held in conjunction with ACL 2004, 25一26 July 2004, Barcelona, Spain. ACL, 2004:33-40. http://www.aclweb.org/anthology/W04-3205.
[19]
MacCartney B, Galley M, Manning C D. A Phrase-Based Alignment Model for Natural Language Inference[C/OL]// 2008 Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, Proceedings of the Conference, 25-27 October 2008, Honolulu, Hawaii, USA, A meeting of SIGDAT, a Special Interest Group of the ACL. [5.1.]:ACL, 2008:802-811.http://www.aclweb.org/anthology/D08-1084.
[20]
Basak R, Naskar S K, Pakray P, Recognizing Textual Entailment by Soft Dependency Tree Matching[J/OL]. Computacion y Sistemas, 2015, 19(4). http://cys.cic.ipn.mx/oj s/index.php/CyS/article/view/2331.
[21]
Sultan M A, Bethard S, Summer T. Feature-Rich Two-Stage Logistic Regression for Monolingual Alignment[C/OL]//Marquez L, Callison-Burch C, Su J, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015. [5.1.]:The Association for Computational Linguistics, 2015:949-959. http://aclweb.org/anthology/D/D 15/D 15一l l l l.pdf.
[22]
Noh T, Pado S, Shwartz从et al. Multi-Level Alignments As An Extensible Representation Basis for Textual Entailment Algorithms[C/OL]// Palmer M, Boleda G, Rosso P. Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, *SEM 2015, June 4-5, 2015, Denver, Colorado, USA. The *SEM 2015 Organizing Committee, 2015:193-198. http://aclweb.org/anthology/S/S 15/S 15一1022.pdf.
[23]
Kalchbrenner N, Grefenstette E, Blunsom P. A Convolutional Neural Network for Modelling Sentences[C/OL]// Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, June 22-27, 2014, Baltimore, MD, USA, Volume 1:Long Papers. The Association for Computer Linguistics, 2014:655-665. http://aclweb.org/anthology/P/P14/P14-1062.pdf.
[24]
Kim Y. Convolutional Neural Networks for Sentence Classification[C/OL]// Moschitti A, Pang B, Daelemans W. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL. ACL, 2014:1746-1751.http://aclweb.org/anthology/D/D 14/D 14-1181.pdf.
[25]
Yin W, Schiitze H, Xiang B, ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs[J/OL]. TACL, 2016, 4:259-272. https://tac12013.cs.columbia.edu/oj s/index.php/tacl/article/view/831.
[26]
Mou L, Men R, Li G, Natural Language Inference by Tree-Based Convolution and Heuristic Matching[C/OL]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 2: Short Papers. The Association for Computer Linguistics, 2016. http://aclweb.org/anthology/P/P16/P16-2022.pdf.
[27]
Mou L, Men R, Li G, Recognizing Entailment and Contradiction by Tree based Convolution[J/OL]. CoRR, 2015, abs/1512.08422. http://arxiv.org/abs/1512.08422.
[28]
Cheng J, Dong L, Lapata M. Long Short-Term Memory-Networks for Machine Reading[C/OL]// Su J, Cameras X, Duh K. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, 2016. The Association for Computational Linguistics, 2016:551-561.http://aclweb.org/anthology/D/D 16/D 16-1053.pdf.
[29]
Sundermeyer M, Schliiter R, Ney H. LSTM Neural Networks for Language Modeling[C/OL]// INTERSPEECH 2012, 13th Annual Conference of the International Speech Communication Association, Portland, Oregon, USA, September 9-13, 2012. ISCA, 2012:194-197. http://www.isca-speech.org/archive/interspeech_2012/i12_0194.htm1.
[30]
MacCartney B, Manning C D. Natural Logic and Natural Language Inference[G] // Computing Meaning. Springer, 2014:129-147.
[31]
Liu Y, Sun C,LSTM model Lin L, Learning Natural Language Inference using Bidirectional and Inner-Attention[J/OL]. CoRR, 2016, abs/1605.09090. http://arxiv.org/abs/1605.09090.
[32]
Wang S, Jiang J. Learning Natural Language Inference with LSTM [C/OL]// Knight K, Nenkova A, Rambow O. NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego California, USA, June 12-17, 2016. [5.1.]:The Association for Computational Linguistics, 2016:1442-1451.http://aclweb.org/anthology/N/N 16/N 16-1170.pdf.
Index Terms
- Research on Recognition Method of Textual Implication
Recommendations
Robust face recognition using generalized neural reflectance model
A generalized neural reflectance (GNR) model for enhancing face recognition under variations in illumination and posture is presented in this paper. Our work is based on training a number of synthesis images of each face taken at single lighting ...
Facial expression recognition using 3D facial feature distances
ICIAR'07: Proceedings of the 4th international conference on Image Analysis and RecognitionIn this paper, we propose a novel approach for facial expression analysis and recognition. The proposed approach relies on the distance vectors retrieved from 3D distribution of facial feature points to classify universal facial expressions. Neural ...
Towards Bridging the Web and the Semantic Web
WI-IAT '09: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01The World Wide Web (WWW) has provided us with a plethora of information. However, given its unstructured format, this information is useful mainly to humans and cannot be effectively interpreted by machines. The Semantic Web provides information in ...
Comments
Information & Contributors
Information
Published In
June 2022
221 pages
ISBN:9781450396295
DOI:10.1145/3546000
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 August 2022
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
HP3C'22
HP3C'22: 2022 6th International Conference on High Performance Compilation, Computing and Communications
June 23 - 25, 2022
Jilin, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 21Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)0
Reflects downloads up to 26 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format