Word Embedding Evaluation Datasets and Wikipedia Title Embedding for Chinese

Abstract

Distributed word representations are widely used in many NLP tasks, and there are lots of benchmarks to evaluate word embeddings in English. However there are barely evaluation sets with large enough amount of data for Chinese word embeddings. Therefore, in this paper, we create several evaluation sets for Chinese word embedding on both word similarity task and analogical task via translating some existing popular evaluation sets from English to Chinese. To assess the quality of translated datasets, we obtain human rating from both experts and Amazon Mechanical Turk workers. While translating the datasets, we find out that around 30 percents of word pairs in the benchmarks are Wikipedia titles. This motivate us to evaluate the performance of Wikipedia title embeddings on our new benchmarks. Thus, in this paper, not only the new benchmarks are tested but some new improved approaches of Wikipedia title embeddings are proposed. We perform training of embeddings of Wikipedia titles using not only their Wikipedia context but also their Wikipedia categories, most of categories are noun phrases, and we identify the head words of the noun phrases by a parser for further emphasizing their roles on the training of title embeddings. Experimental results and the comprehensive error analysis demonstrate that the benchmarks can precisely reflect the approaches' quality, and the effectiveness of our improved approaches on Wikipedia title embeddings are also verified and analyzed in detail.

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