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Open Access

How Do Pronouns Affect Word Embedding

Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
School of Computer Science and Technology, University of South China, Hengyang 421001, China.
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Abstract

Word embedding has drawn a lot of attention due to its usefulness in many NLP tasks. So far a handful of neural-network based word embedding algorithms have been proposed without considering the effects of pronouns in the training corpus. In this paper, we propose using co-reference resolution to improve the word embedding by extracting better context. We evaluate four word embeddings with considerations of co-reference resolution and compare the quality of word embedding on the task of word analogy and word similarity on multiple data sets. Experiments show that by using co-reference resolution, the word embedding performance in the word analogy task can be improved by around 1.88 %. We find that the words that are names of countries are affected the most, which is as expected.

Keywords

word embeddingco-reference resolutionrepresentation learning

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Tsinghua Science and Technology
Volume 22 Issue 6,
December 2017
Pages 586-594
DOI: 10.23919/TST.2017.8195342
Cite this article:
Chung T, Xu B, Liu Y, et al. How Do Pronouns Affect Word Embedding. Tsinghua Science and Technology, 2017, 22(6): 586-594. https://doi.org/10.23919/TST.2017.8195342

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Received: 31 December 2016
Revised: 29 March 2017
Accepted: 25 May 2017
Published: 14 December 2017
© The author(s) 2017
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