Multiplex Word Embeddings for Selectional Preference Acquisition

TL;DR

This paper introduces a multiplex word embedding model that captures relational dependencies among words, improving semantic and relational understanding while maintaining compactness and scalability.

Contribution

The proposed model extends traditional embeddings by incorporating multiple relation-specific embeddings, enabling better distinction of words under different syntactic relations.

Findings

Effective in selectional preference acquisition

Achieves better performance with smaller embedding size

Scalable to large vocabularies

Abstract

Conventional word embeddings represent words with fixed vectors, which are usually trained based on co-occurrence patterns among words. In doing so, however, the power of such representations is limited, where the same word might be functionalized separately under different syntactic relations. To address this limitation, one solution is to incorporate relational dependencies of different words into their embeddings. Therefore, in this paper, we propose a multiplex word embedding model, which can be easily extended according to various relations among words. As a result, each word has a center embedding to represent its overall semantics, and several relational embeddings to represent its relational dependencies. Compared to existing models, our model can effectively distinguish words with respect to different relations without introducing unnecessary sparseness. Moreover, to accommodate various relations, we use a small dimension for relational embeddings and our model is able to keep their effectiveness. Experiments on selectional preference acquisition and word similarity demonstrate the effectiveness of the proposed model, and a further study of scalability also proves that our embeddings only need 1/20 of the original embedding size to achieve better performance.