Editing WordNet (section)

== Limitations ==
The most widely discussed limitation of WordNet (and related resources like [[ImageNet]]) is that some of the [[semantic relation]]s are more suited to concrete concepts than to abstract concepts.<ref>{{cite journal |last1=Rudnicka |first1=Ewa |last2=Bond |first2=Francis |last3=Grabowski |first3=Łukasz |last4=Piasecki |first4=Maciej |last5=Piotrowski |first5=Tadeusz |title=Lexical Perspective on Wordnet to Wordnet Mapping |journal=Proceedings of the 9th Global WordNet Conference (GWC 2018) |date=2018 |page=210}}</ref> For example, it is easy to create hyponyms/hypernym relationships to capture that a "[[conifer]]" is a type of "[[tree]]", a "tree" is a type of "[[plant]]", and a "plant" is a type of "[[organism]]", but it is difficult to classify emotions like "fear" or "happiness" into equally deep and well-defined hyponyms/hypernym relationships.

Many of the concepts in WordNet are specific to certain languages and the most accurate reported mapping between languages is 94%.<ref>{{cite journal |last1=Bond |first1=Francis |last2=Foster |first2=Ryan |title=Linking and Extending an Open Multilingual Wordnet |journal=Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics |date=2013 |pages=1352–1362 |url=https://www.aclweb.org/anthology/P13-1133.pdf |access-date=20 January 2020}}</ref> Synonyms, hyponyms, meronyms, and antonyms occur in all languages with a WordNet so far, but other semantic relationships are language-specific.<ref>{{cite journal |last1=Fellbaum |first1=Christiane |last2=Vossen |first2=Piek |title=Challenges for a multilingual wordnet |journal=Language Resources and Evaluation |date=2012 |volume=46 |issue=2 |pages=313–326|doi=10.1007/s10579-012-9186-z |s2cid=10117946 }}</ref> This limits the interoperability across languages. However, it also makes WordNet a resource for highlighting and studying the differences between languages, so it is not necessarily a limitation for all use cases.

WordNet does not include information about the [[etymology]] or the pronunciation of words and it contains only limited information about usage. WordNet aims to cover most everyday words and does not include much domain-specific terminology.

WordNet is the most commonly used computational lexicon of English for [[word-sense disambiguation]] (WSD), a task aimed at assigning the context-appropriate meanings (i.e. synset members) to words in a text.<ref>R. Navigli. [http://www.dsi.uniroma1.it/~navigli/pubs/ACM_Survey_2009_Navigli.pdf Word Sense Disambiguation: A Survey], ''ACM Computing Surveys'', 41(2), 2009, pp. 1–69</ref> However, it has been argued that WordNet encodes sense distinctions that are too fine-grained. This issue prevents WSD systems from achieving a level of performance comparable to that of humans, who do not always agree when confronted with the task of selecting a sense from a dictionary that matches a word in a context. The granularity issue has been tackled by proposing [[cluster analysis|clustering]] methods that automatically group together similar senses of the same word.<ref>E. Agirre, O. Lopez. 2003.
Clustering WordNet Word Senses. In ''Proc. of the Conference on Recent Advances on Natural Language (RANLP’03)'', Borovetz, Bulgaria, pp. 121–130.</ref><ref>R. Navigli. [http://acl.ldc.upenn.edu/P/P06/P06-1014.pdf Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance], In ''Proc. of the 44th Annual Meeting of the Association for Computational Linguistics joint with the 21st International Conference on Computational Linguistics (COLING-ACL 2006)'', Sydney, Australia, July 17-21st, 2006, pp. 105–112.</ref><ref>R. Snow, S. Prakash, D. Jurafsky, A. Y. Ng. 2007. [http://www.aclweb.org/anthology/D/D07/D07-1107.pdf Learning to Merge Word Senses], ''In Proc. of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)'', Prague, Czech Republic, pp. 1005–1014.</ref>

=== Offensive content ===
WordNet includes words that can be perceived as [[pejorative]] or offensive.<ref>{{Cite web |last=Wong |first=Julia Carrie |author-link=Julia Carrie Wong |date=2019-09-18 |title=The viral selfie app ImageNet Roulette seemed fun – until it called me a racist slur |url=http://www.theguardian.com/technology/2019/sep/17/imagenet-roulette-asian-racist-slur-selfie |access-date=2022-10-14 |website=the Guardian |language=en}}</ref> The interpretation of a word can [[Semantic change|change over time]] and [[In-group and out-group|between social groups]], so it is not always possible for WordNet to define a word as "[[pejorative]]" or "offensive" in isolation. Therefore, people using WordNet must apply their own methods to identify offensive or pejorative words.

However, this limitation is true of other lexical resources like [[dictionary|dictionaries]] and [[thesaurus]]es, which also contain [[pejorative]] and offensive words. Some dictionaries indicate words that are [[pejorative]]s, but do not include all the contexts in which words might be acceptable or offensive to different social groups. Therefore, people using dictionaries must apply their own methods to identify all offensive words.

=== Licensed vs. Open WordNets ===
Some wordnets were subsequently created for other languages. A 2012 survey lists the wordnets and their availability.<ref>Francis Bond and Kyonghee Paik 2012a. [http://web.mysites.ntu.edu.sg/fcbond/open/pubs/2012-gwc-wn-license.pdf A survey of wordnets and their licenses]. In Proceedings of the 6th Global WordNet Conference (GWC 2012). Matsue. 64–71</ref> In an effort to propagate the usage of WordNets, the Global WordNet community had been slowly re-licensing their WordNets to an open domain where researchers and developers can easily access and use WordNets as language resources to provide [[ontology|ontological]] and [[lexicon|lexical]] knowledge in [[natural language processing|natural-language processing]] (NLP) tasks.

The Open Multilingual WordNet<ref>{{cite web|url=http://compling.hss.ntu.edu.sg/omw/|title=Open Multilingual Wordnet|website=compling.hss.ntu.edu.sg|access-date=10 April 2018}}</ref> provides access to [[Open-source license|open licensed]] wordnets in a variety of languages, all linked to the Princeton Wordnet of English (PWN). The goal is to make it easy to use wordnets in multiple languages.