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        <title>Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
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            <namePart type="given">Julia</namePart>
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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.</abstract>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space
%A Washio, Koki
%A Kato, Tsuneaki
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F washio-kato-2018-neural
%X 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.
%R 10.18653/v1/D18-1058
%U https://aclanthology.org/D18-1058
%U https://doi.org/10.18653/v1/D18-1058
%P 594-600

Markdown (Informal)

[Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space](https://aclanthology.org/D18-1058) (Washio & Kato, EMNLP 2018)

• Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space (Washio & Kato, EMNLP 2018)

ACL

• Koki Washio and Tsuneaki Kato. 2018. Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 594–600, Brussels, Belgium. Association for Computational Linguistics.