Editing WordNet (section)

== Applications ==
WordNet has been used for a number of purposes in information systems, including [[word-sense disambiguation]], [[information retrieval]], [[Document classification|automatic text classification]], [[automatic summarization|automatic text summarization]], [[machine translation]] and even automatic crossword puzzle generation.

A common use of WordNet is to determine the [[semantic similarity|similarity]] between words. Various algorithms have been proposed, including measuring the distance among words and synsets in WordNet's graph structure, such as by counting the number of edges among synsets. The intuition is that the closer two words or synsets are, the closer their meaning. A number of WordNet-based word similarity algorithms are implemented in a [[Perl]] package called WordNet::Similarity,<ref>{{cite web|url=http://www.d.umn.edu/~tpederse/similarity.html |title=Ted Pedersen - WordNet::Similarity |publisher=D.umn.edu |date=2008-06-16 |access-date=2014-03-11}}</ref> and in a [[Python (programming language)|Python]] package called [[NLTK]].<ref>[https://likegeeks.com/nlp-tutorial-using-python-nltk NLP using Python NLTK]/</ref> Other more sophisticated WordNet-based similarity techniques include ADW,<ref>M. T. Pilehvar, D. Jurgens and R. Navigli.  [http://wwwusers.di.uniroma1.it/~navigli/pubs/ACL_2013_Pilehvar_Jurgens_Navigli.pdf Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity.]. Proc. of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Sofia, Bulgaria, August 4–9, 2013, pp. 1341-1351.</ref> whose implementation is available in [[Java (programming language)|Java]]. WordNet can also be used to inter-link other vocabularies.<ref>{{cite journal  |vauthors=Ballatore A, etal |volume=20|issue=2| arxiv=1404.5372| journal=Annals of GIS |title=Linking geographic vocabularies through WordNet |date=2014|pages=73–84|doi=10.1080/19475683.2014.904440|bibcode=2014AnGIS..20...73B|s2cid=9246582}}</ref>