Recent studies show that word embedding models often underestimate similarities between similar words and overestimate similarities between distant words. This results in word similarity results obtained from embedding models inconsistent with human judgment. Manifold learningbased methods are widely utilized to refine word representations by re-embedding word vectors from the original embedding space to a new refined semantic space. These methods mainly focus on preserving local geometry information through performing weighted locally linear combination between words and their neighbors twice. However, these reconstruction weights are easily influenced by different selections of neighboring words and the whole combination process is time-consuming. In this paper, we propose two novel word representation refinement methods leveraging isometry feature mapping and local tangent space respectively. Unlike previous methods, our first method corrects pre-trained word embeddings by preserving global geometry information of all words instead of local geometry information between words and their neighbors. Our second method refines word representations by aligning original and refined embedding spaces based on local tangent space instead of performing weighted locally linear combination twice. Experimental results obtained from standard semantic relatedness and semantic similarity tasks show that our methods outperform various state-of-the-art baselines for word representation refinement.

Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information

LONG23: Semantics 2

technical paper

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Wenyu Zhao

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Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information

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