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Understanding the source of semantic regularities in word embeddings

Chiang, Hsiao-Yu, Camacho-Collados, Jose ORCID: and Pardos, Zachary 2020. Understanding the source of semantic regularities in word embeddings. Presented at: SIGNLL Conference Computational Natural Language Learning (CoNLL 2020), Virtual, 19-20 November 2020. Proceedings of the 24th Conference on Computational Natural Language Learning. Association for Computational Linguistics, pp. 119-131.

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Semantic relations are core to how humans understand and express concepts in the real world using language. Recently, there has been a thread of research aimed at modeling these relations by learning vector representations from text corpora. Most of these approaches focus strictly on leveraging the co-occurrences of relationship word pairs within sentences. In this paper, we investigate the hypothesis that examples of a lexical relation in a corpus are fundamental to a neural word embedding’s ability to complete analogies involving the relation. Our experiments, in which we remove all known examples of a relation from training corpora, show only marginal degradation in analogy completion performance involving the removed relation. This finding enhances our understanding of neural word embeddings, showing that co-occurrence information of a particular semantic relation is the not the main source of their structural regularity.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Additional Information: Creative Commons Attribution 4.0 International License
Publisher: Association for Computational Linguistics
Date of First Compliant Deposit: 15 December 2020
Date of Acceptance: 18 September 2020
Last Modified: 09 Nov 2022 09:47

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