From syntactic structure to semantic relationship: hypernym extraction from definitions by recurrent neural networks using the part of speech information
This work aims to improve the accuracy and applicability of hypernym extraction for NLP and semantic analysis, particularly for domain-specific applications where current resources are lacking.
This paper addresses the problem of extracting hypernyms from definitions, a crucial task for building semantic networks, especially in domain-specific contexts where public dictionaries are insufficient. The authors propose a recurrent neural network approach that incorporates part-of-speech information to overcome limitations of existing pattern-based or word-representation-focused methods.
The hyponym-hypernym relation is an essential element in the semantic network. Identifying the hypernym from a definition is an important task in natural language processing and semantic analysis. While a public dictionary such as WordNet works for common words, its application in domain-specific scenarios is limited. Existing tools for hypernym extraction either rely on specific semantic patterns or focus on the word representation, which all demonstrate certain limitations.