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Impact of Target Word and Context on End-to-End Metonymy Detection ...
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Abstract:
Metonymy is a figure of speech in which an entity is referred to by another related entity. The task of metonymy detection aims to distinguish metonymic tokens from literal ones. Until now, metonymy detection methods attempt to disambiguate only a single noun phrase in a sentence, typically location names or organization names. In this paper, we disambiguate every word in a sentence by reformulating metonymy detection as a sequence labeling task. We also investigate the impact of target word and context on metonymy detection. We show that the target word is less useful for detecting metonymy in our dataset. On the other hand, the entity types that are associated with domain-specific words in their context are easier to solve. This shows that the context words are much more relevant for detecting metonymy. ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2112.03256 https://dx.doi.org/10.48550/arxiv.2112.03256
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A Novel Wikipedia based Dataset for Monolingual and Cross-Lingual Summarization ...
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Countering the Influence of Essay Length in Neural Essay Scoring ...
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Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation ...
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Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers ...
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Lexical Features in Coreference Resolution: To be Used With Caution ...
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Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm
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In: Urban Planning ; 1 ; 2 ; 114-127 (2017)
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Toward independent L2 readers: Effects of text adjuncts, subject knowledge, L1 reading, and L2 proficiency
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A Meta-Analysis of Mentalizing Impairments in Adults With Schizophrenia and Autism Spectrum Disorder
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