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 396 articles , 1 standard article )

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  1. Morales, Pedro Ramaciotti; Lamarche-Perrin, Robin; Fournier-S’niehotta, Raphaël; Poulain, Rémy; Tabourier, Lionel; Tarissan, Fabien: Measuring diversity in heterogeneous information networks (2021)
  2. Carmona, Josep; Padró, Lluís; Delicado, Luis: Flexible process model mapping using relaxation labeling (2020)
  3. Dillon Niederhut: niacin: A Python package for text data enrichment (2020) not zbMATH
  4. Fürnkranz, Johannes; Kliegr, Tomáš; Paulheim, Heiko: On cognitive preferences and the plausibility of rule-based models (2020)
  5. Han, Yongming; Chen, Guofei; Li, Zhongkun; Geng, Zhiqiang; Li, Fang; Ma, Bo: An asymmetric knowledge representation learning in manifold space (2020)
  6. Liberti, Leo: Distance geometry and data science (2020)
  7. Nakayama, Atsuho; Baier, Daniel: Predicting brand confusion in imagery markets based on deep learning of visual advertisement content (2020)
  8. Rui, Xiaobin; Yang, Xiaodong; Fan, Jianping; Wang, Zhixiao: A neighbour scale fixed approach for influence maximization in social networks (2020)
  9. Tikhomirov, M. M.; Loukachevitch, N. V.; Dobrov, B. V.: Recognizing named entities in specific domain (2020)
  10. Vanzo, Andrea; Croce, Danilo; Bastianelli, Emanuele; Basili, Roberto; Nardi, Daniele: Grounded language interpretation of robotic commands through structured learning (2020)
  11. Zhang, Richong; Mao, Yongyi; Zhao, Weihua: Knowledge graphs completion via probabilistic reasoning (2020)
  12. Agerri, Rodrigo; Rigau, German: Language independent sequence labelling for opinion target extraction (2019)
  13. Claudia Schon, Sophie Siebert, Frieder Stolzenburg: Using ConceptNet to Teach Common Sense to an Automated Theorem Prover (2019) arXiv
  14. Evert, Stefan; Heinrich, Philipp; Henselmann, Klaus; Rabenstein, Ulrich; Scherr, Elisabeth; Schmitt, Martin; Schröder, Lutz: Combining machine learning and semantic features in the classification of corporate disclosures (2019)
  15. Furbach, Ulrich; Krämer, Teresa; Schon, Claudia: Names are not just sound and smoke: word embeddings for axiom selection (2019)
  16. Jain, Gauri; Sharma, Manisha; Agarwal, Basant: Spam detection in social media using convolutional and long short term memory neural network (2019)
  17. Li, Juan; Zhang, Wen; Chen, Huajun: Incorporating domain and range of relations for knowledge graph completion (2019)
  18. Nie, Binling; Sun, Shouqian: Context-dependent representation of knowledge graphs (2019)
  19. Niu, Feng gao: Basic co-occurrence latent semantic vector space model (2019)
  20. Raggi, Daniel; Stockdill, Aaron; Jamnik, Mateja; Garcia Garcia, Grecia; Sutherland, Holly E. A.; Cheng, Peter C.-H.: Inspection and selection of representations (2019)

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