WordNet® is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations. The resulting network of meaningfully related words and concepts can be navigated with the browser. WordNet is also freely and publicly available for download. WordNet’s structure makes it a useful tool for computational linguistics and natural language processing. WordNet superficially resembles a thesaurus, in that it groups words together based on their meanings. However, there are some important distinctions. First, WordNet interlinks not just word forms—strings of letters—but specific senses of words. As a result, words that are found in close proximity to one another in the network are semantically disambiguated. Second, WordNet labels the semantic relations among words, whereas the groupings of words in a thesaurus does not follow any explicit pattern other than meaning similarity.

References in zbMATH (referenced in 412 articles , 1 standard article )

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  1. Bigot, Jérémie; Deledalle, Charles: Low-rank matrix denoising for count data using unbiased Kullback-Leibler risk estimation (2022)
  2. Loureiro, Daniel; Mário Jorge, Alípio; Camacho-Collados, Jose: LMMS reloaded: transformer-based sense embeddings for disambiguation and beyond (2022)
  3. Su, Yuling; Zhao, Hong; Lin, Yaojin: Few-shot learning based on hierarchical classification via multi-granularity relation networks (2022)
  4. Vrublevskyi, V.; Marchenko, O.: Development and analysis of a sentence semantics representation model (2022)
  5. Ayats, Hugo; Cellier, Peggy; Ferré, Sébastien: Extracting relations in texts with concepts of neighbours (2021)
  6. Confalonieri, Roberto; Weyde, Tillman; Besold, Tarek R.; Moscoso del Prado Martín, Fermín: Using ontologies to enhance human understandability of global post-hoc explanations of black-box models (2021)
  7. Cozman, Fabio Gagliardi; Munhoz, Hugo Neri: Some thoughts on knowledge-enhanced machine learning (2021)
  8. Liu, Xinxin; Zhou, Yucan; Zhao, Hong: Robust hierarchical feature selection driven by data and knowledge (2021)
  9. Loukachevitch, N. V.; Tikhomirov, M. M.; Parkhomenko, E. A.: Using embedding-based similarities to improve lexical resources (2021)
  10. Morales, Pedro Ramaciotti; Lamarche-Perrin, Robin; Fournier-S’niehotta, Raphaël; Poulain, Rémy; Tabourier, Lionel; Tarissan, Fabien: Measuring diversity in heterogeneous information networks (2021)
  11. Ostovar, Ahmad; Bensch, Suna; Hellström, Thomas: Natural language guided object retrieval in images (2021)
  12. Purver, Matthew; Sadrzadeh, Mehrnoosh; Kempson, Ruth; Wijnholds, Gijs; Hough, Julian: Incremental composition in distributional semantics (2021)
  13. Shan, Ruocheng; Youssef, Abdou: Towards math terms disambiguation using machine learning (2021)
  14. Xu, Mengjia: Understanding graph embedding methods and their applications (2021)
  15. Yan, Xiaohan; Bien, Jacob: Rare feature selection in high dimensions (2021)
  16. Carmona, Josep; Padró, Lluís; Delicado, Luis: Flexible process model mapping using relaxation labeling (2020)
  17. Dillon Niederhut: niacin: A Python package for text data enrichment (2020) not zbMATH
  18. Dutta, Anjan; Akata, Zeynep: Semantically tied paired cycle consistency for any-shot sketch-based image retrieval (2020)
  19. Fürnkranz, Johannes; Kliegr, Tomáš; Paulheim, Heiko: On cognitive preferences and the plausibility of rule-based models (2020)
  20. Han, Yongming; Chen, Guofei; Li, Zhongkun; Geng, Zhiqiang; Li, Fang; Ma, Bo: An asymmetric knowledge representation learning in manifold space (2020)

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