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Structurally enriched entity mention embedding from semi-structured textual content.

, , и . SAC, стр. 857-860. ACM, (2021)

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Yin Yang

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Structurally enriched entity mention embedding from semi-structured textual content., , и . SAC, стр. 857-860. ACM, (2021)GenSense: A Generalized Sense Retrofitting Model., , , , и . COLING, стр. 1662-1671. Association for Computational Linguistics, (2018)Enconter: Entity Constrained Progressive Sequence Generation via Insertion-based Transformer., , и . EACL, стр. 3590-3599. Association for Computational Linguistics, (2021)MSD-1030: A Well-built Multi-Sense Evaluation Dataset for Sense Representation Models., , , , и . LREC, стр. 5802-5809. European Language Resources Association, (2020)Exploring Ensemble of Models in Taxonomy-based Cross-Domain Sentiment Classification., , , и . CIKM, стр. 1279-1288. ACM, (2014)Taxonomy-based regression model for cross-domain sentiment classification., , , и . CIKM, стр. 1557-1560. ACM, (2013)Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement., , , , и . J. Assoc. Inf. Sci. Technol., 71 (6): 657-670 (2020)That Makes Sense: Joint Sense Retrofitting from Contextual and Ontological Information., , , и . WWW (Companion Volume), стр. 15-16. ACM, (2018)NTUNLP approaches to recognizing and disambiguating entities in long and short text at the ERD challenge 2014., , , , , , и . ERD@SIGIR, стр. 3-12. ACM, (2014)Combining Word Embedding and Lexical Database for Semantic Relatedness Measurement., , , и . WWW (Companion Volume), стр. 73-74. ACM, (2016)