Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space

Koki Washio; Tsuneaki Kato

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arXiv · 1809.03401

Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space

Koki Washio·Tsuneaki Kato

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Abstract

Capturing the semantic relations of words in a vector space contributes to many natural language processing tasks. One promising approach exploits lexico-syntactic patterns as features of word pairs. In this paper, we propose a novel model of this pattern-based approach, neural latent relational analysis (NLRA). NLRA can generalize co-occurrences of word pairs and lexico-syntactic patterns, and obtain embeddings of the word pairs that do not co-occur. This overcomes the critical data sparseness problem encountered in previous pattern-based models. Our experimental results on measuring relational similarity demonstrate that NLRA outperforms the previous pattern-based models. In addition, when combined with a vector offset model, NLRA achieves a performance comparable to that of the state-of-the-art model that exploits additional semantic relational data.

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BibTeX RIS

Koki Washio, Tsuneaki Kato. 2018-09-10. Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space. https://arxiv.org/abs/1809.03401

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