Distributed Word Representations Improve NER for e-Commerce

Abstract

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.