分布式词表示改进电子商务的NER

VS@HLT-NAACL Pub Date : 2015-06-01 DOI:10.3115/v1/W15-1522

Mahesh Joshi, Ethan Hart, Mirko Vogel, Jean-David Ruvini

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引用次数: 20

摘要

本文介绍了一个使用分布式词表示(特别是word2vec)来提高电子商务领域命名实体识别性能的案例研究。我们还证明了在少量域内数据上训练的分布式词表示比在大量域外数据上训练的词向量更有效，并且它们的组合给出了最好的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Distributed Word Representations Improve NER for e-Commerce

This paper presents a case study of using distributed word representations, word2vec in particular, for improving performance of Named Entity Recognition for the eCommerce domain. We also demonstrate that distributed word representations trained on a smaller amount of in-domain data are more effective than word vectors trained on very large amount of out-of-domain data, and that their combination gives the best results.

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