Learning Word-pair Embeddings from Co-occurrences of Word Pairs and Lexico-syntactic Patterns to Recognize Semantic Relations between Nouns

Abstract

Recognizing lexical semantic relations of word pairs, especially noun pairs, is an important task for automatic completion and expansion of lexical knowledge bases, such as WordNet, which can be used for natural language understanding. One of the promising approaches to this task is the utilization of lexico-syntactic patterns co-occurring with target word pairs, which reflect their lexical semantic relations. These pattern-based methods require co-occurrences of the target word pairs. However, this requirement is hardly satisfied because of Zipf’s law that states, which most content words occur very rarely. To solve this problem, we propose a novel unsupervised learning method to obtain word-pair embeddings that reflect co-occurring lexico-syntactic patterns. In recognizing lexical semantic relations, our method provides relational information for word pairs that do not co-occur in a corpus because the neural network generalizes the co-occurrence between word pairs and lexico-syntactic patterns. The experimental results show that our word-pair embeddings improved the performance of the state-of-the-art neural pattern-based method on the noun pairs of four datasets and successfully alleviated the co-occurrence issue.