WordNet

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 392 articles , 1 standard article )

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  1. Dillon Niederhut: niacin: A Python package for text data enrichment (2020) not zbMATH
  2. Fürnkranz, Johannes; Kliegr, Tomáš; Paulheim, Heiko: On cognitive preferences and the plausibility of rule-based models (2020)
  3. Liberti, Leo: Distance geometry and data science (2020)
  4. Tikhomirov, M. M.; Loukachevitch, N. V.; Dobrov, B. V.: Recognizing named entities in specific domain (2020)
  5. Vanzo, Andrea; Croce, Danilo; Bastianelli, Emanuele; Basili, Roberto; Nardi, Daniele: Grounded language interpretation of robotic commands through structured learning (2020)
  6. Agerri, Rodrigo; Rigau, German: Language independent sequence labelling for opinion target extraction (2019)
  7. Claudia Schon, Sophie Siebert, Frieder Stolzenburg: Using ConceptNet to Teach Common Sense to an Automated Theorem Prover (2019) arXiv
  8. 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)
  9. Furbach, Ulrich; Krämer, Teresa; Schon, Claudia: Names are not just sound and smoke: word embeddings for axiom selection (2019)
  10. Jain, Gauri; Sharma, Manisha; Agarwal, Basant: Spam detection in social media using convolutional and long short term memory neural network (2019)
  11. Li, Juan; Zhang, Wen; Chen, Huajun: Incorporating domain and range of relations for knowledge graph completion (2019)
  12. Nie, Binling; Sun, Shouqian: Context-dependent representation of knowledge graphs (2019)
  13. Niu, Feng gao: Basic co-occurrence latent semantic vector space model (2019)
  14. Raggi, Daniel; Stockdill, Aaron; Jamnik, Mateja; Garcia Garcia, Grecia; Sutherland, Holly E. A.; Cheng, Peter C.-H.: Inspection and selection of representations (2019)
  15. Rubio-Manzano, Clemente; Pereira-Fariña, Martín: On the incorporation of interval-valued fuzzy sets into the Bousi-Prolog system: declarative semantics, implementation and applications (2019)
  16. Sharma, Arpit: Using answer set programming for commonsense reasoning in the Winograd schema challenge (2019)
  17. Song, Yangqiu; Upadhyay, Shyam; Peng, Haoruo; Mayhew, Stephen; Roth, Dan: Toward any-language zero-shot topic classification of textual documents (2019)
  18. Wang, Yashen; Zhang, Huanhuan; Xie, Haiyong: Geography-enhanced link prediction framework for knowledge graph completion (2019)
  19. Williams, Lowri; Arribas-Ayllon, Michael; Artemiou, Andreas; Spasić, Irena: Comparing the utility of different classification schemes for emotive language analysis (2019)
  20. Zheng, Weiguo; Cheng, Hong; Yu, Jeffrey Xu; Zou, Lei; Zhao, Kangfei: Interactive natural language question answering over knowledge graphs (2019)

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