From syntactic structure to semantic relationship: hypernym extraction from definitions by recurrent neural networks using the part of speech information

TL;DR

This paper proposes a neural network-based method that leverages part of speech information to extract hypernyms from definitions, aiming to improve semantic relationship identification especially in domain-specific contexts.

Contribution

It introduces a recurrent neural network approach that incorporates part of speech data for hypernym extraction, addressing limitations of previous pattern-based and word representation methods.

Findings

Effective hypernym extraction from definitions demonstrated

Improved performance over existing methods in domain-specific scenarios

Utilizes part of speech information for better semantic understanding

Abstract

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.