Editing Semantic similarity (section)

=== In natural language processing ===
[[Natural language processing]] (NLP) is a field of computer science and linguistics. Sentiment analysis, Natural language understanding and Machine translation (Automatically translate text from one human language to another) are a few of the major areas where it is being used. For example, knowing one information resource in the internet, it is often of immediate interest to find similar resources. The [[Semantic Web]] provides semantic extensions to find similar data by content and not just by arbitrary descriptors.<ref>[http://www.di.uniba.it/~cdamato/PhDThesis_dAmato.pdf Similarity-based Learning Methods for the Semantic Web] (C. d'Amato, PhD Thesis)</ref><ref>{{cite journal|author1=Gracia, J. |author2=Mena, E. |name-list-style=amp |year=2008|url=http://disi.unitn.it/~p2p/RelatedWork/Matching/Gracia_wise08.pdf|title=Web-Based Measure of Semantic Relatedness|journal=Proceedings of the 9th International Conference on Web Information Systems Engineering (WISE '08)|pages=136–150}}</ref><ref>Raveendranathan, P. (2005). [http://www.d.umn.edu/~tpederse/Pubs/prath-thesis.pdf Identifying Sets of Related Words from the World Wide Web]. Master of Science Thesis, University of Minnesota Duluth.</ref><ref>Wubben, S. (2008). [http://ilk.uvt.nl/~swubben/publications/wubben2008-techrep.pdf Using free link structure to calculate semantic relatedness]. In ILK Research Group Technical Report Series, nr. 08-01, 2008.</ref><ref>Juvina, I., van Oostendorp, H., Karbor, P., & Pauw, B. (2005). [https://cloudfront.escholarship.org/dist/prd/content/qt0p7528tp/qt0p7528tp.pdf Towards modeling contextual information in web navigation]. In B. G. Bara & L. Barsalou & M. Bucciarelli (Eds.), 27th Annual Meeting of the Cognitive Science Society, CogSci2005 (pp.&nbsp;1078–1083). Austin, Tx: The Cognitive Science Society, Inc.</ref><ref>Navigli, R., Lapata, M. (2007). [http://www.aaai.org/Papers/IJCAI/2007/IJCAI07-272.pdf Graph Connectivity Measures for Unsupervised Word Sense Disambiguation], Proc. of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), Hyderabad, India, January 6–12th, 2007, pp.&nbsp;1683–1688.</ref><ref>{{cite journal|author=Pirolli, P.|year=2005|title=Rational analyses of information foraging on the Web|journal=Cognitive Science|volume=29|issue=3|pages=343–373|doi=10.1207/s15516709cog0000_20|pmid=21702778|doi-access=free}}</ref><ref>{{cite book|author=Pirolli, P.|author2=Fu, W.-T.|name-list-style=amp |year=2003|chapter=SNIF-ACT: A model of information foraging on the World Wide Web|title=Lecture Notes in Computer Science|volume=2702|pages=45–54|doi=10.1007/3-540-44963-9_8|isbn=978-3-540-40381-4|citeseerx=10.1.1.6.1506}}</ref><ref>Turney, P. (2001). [https://arxiv.org/abs/cs/0212033 Mining the Web for Synonyms: PMI versus LSA on TOEFL]. In L. De Raedt & P. Flach (Eds.), Proceedings of the Twelfth European Conference on Machine Learning (ECML-2001) (pp.&nbsp;491–502). Freiburg, Germany.</ref> [[Deep learning]] methods have become an accurate way to gauge semantic similarity between two text passages, in which each passage is first embedded into a continuous vector representation.<ref>{{Cite book|last1=Reimers|first1=Nils|last2=Gurevych|first2=Iryna|title=Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) |chapter=Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks |date=November 2019|chapter-url=https://www.aclweb.org/anthology/D19-1410|location=Hong Kong, China|publisher=Association for Computational Linguistics|pages=3982–3992|doi=10.18653/v1/D19-1410|arxiv=1908.10084|doi-access=free}}</ref><ref>{{Cite journal|last1=Mueller|first1=Jonas|last2=Thyagarajan|first2=Aditya|date=2016-03-05|title=Siamese Recurrent Architectures for Learning Sentence Similarity|url=https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12195|journal=Thirtieth AAAI Conference on Artificial Intelligence|volume=30 |doi=10.1609/aaai.v30i1.10350 |s2cid=16657628 |language=en|doi-access=free}}</ref><ref>{{Citation|last1=Kiros|first1=Ryan|title=Skip-Thought Vectors|date=2015|url=http://papers.nips.cc/paper/5950-skip-thought-vectors.pdf|work=Advances in Neural Information Processing Systems 28|pages=3294–3302|editor-last=Cortes|editor-first=C.|publisher=Curran Associates, Inc.|access-date=2020-03-13|last2=Zhu|first2=Yukun|last3=Salakhutdinov|first3=Russ R|last4=Zemel|first4=Richard|last5=Urtasun|first5=Raquel|last6=Torralba|first6=Antonio|last7=Fidler|first7=Sanja|editor2-last=Lawrence|editor2-first=N. D.|editor3-last=Lee|editor3-first=D. D.|editor4-last=Sugiyama|editor4-first=M.}}</ref>