Utilizing Local Tangent Information for Word Re-embedding

Word embedding models typically learn dense and fixed-length vectors based on local word collocation patterns in a text corpus. Recent studies have discovered that these models often underestimate similarities between similar words and overestimate similarities between distant words. This leads to word similarity results obtained from word embedding models inconsistent with human judgment. A number of manifold learning-based word re-embedding methods are proposed to address this problem by re-embedding word vectors from the original embedding space to a new embedding space. However, these methods perform a weighted locally linear combination of embeddings of words and their neighbors twice. Besides, the reconstruction weights are easily influenced by the selection of word neighbors and the whole combination process is very time-consuming. In this paper, we introduce a novel word re-embedding method based on local tangent information to re-embed word vectors into a refined new space. Unlike previous approaches, our method re-embeds word vectors by aligning original and new embedding spaces based on the tangent information instead of performing weighted locally linear combination twice. To validate the proposed method, experiments were conducted on two standard evaluation tasks. The experimental results show that our method achieves better performance than state-of-the-art methods for word re-embedding.