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Semantic Data Set Construction from Human Clustering and Spatial Arrangement ...
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Context vs Target Word: Quantifying Biases in Lexical Semantic Datasets ...
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AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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Quantifying lexical usage: vocabulary pertaining to ecosystems and the environment
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Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ...
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Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ...
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Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis
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Majewska, Olga; Vulic, Ivan; McCarthy, Diana. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.423, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020
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Investigating the cross-lingual translatability of VerbNet-style classification. ...
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Investigating the cross-lingual translatability of VerbNet-style classification.
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Word Sense Clustering and Clusterability
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In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01838502 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2016, 42, pp.245-275. ⟨10.1162/COLI⟩ (2016)
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Integrating character representations into Chinese word embedding
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Abstract:
In this paper we propose a novel word representation for Chinese based on a state-of-the-art word embedding approach. Our main contribution is to integrate distributional representations of Chinese characters into the word embedding. Recent related work on European languages has demonstrated that information from inflectional morphology can reduce the problem of sparse data and improve word representations. Chinese has very little inflectional morphology, but there is potential for incorporating character-level information. Chinese characters are drawn from a fixed set – with just under four thousand in common usage – but a major problem with using characters is their ambiguity. In order to address this problem, we disambiguate the characters according to groupings in a semantic hierarchy. Coupling our character embeddings with word embeddings, we observe improved performance on the tasks of finding synonyms and rating word similarity compared to a model using word embeddings alone, especially for low frequency words.
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Keyword:
QA0075 Electronic computers. Computer science
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URL: https://doi.org/10.1007/978-3-319-49508-8_32 http://sro.sussex.ac.uk/id/eprint/66865/
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Semantic clustering of pivot paraphrases
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In: International Conference on Language Resources and Evaluation ; https://hal.archives-ouvertes.fr/hal-01838559 ; International Conference on Language Resources and Evaluation, Jan 2014, Reykjavik, Iceland (2014)
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Quantifying lexical usage: vocabulary pertaining to ecosystems and the environment
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Finding Meaning in Context Using Graph Algorithms in Mono- and Cross-lingual Settings
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