Dictionaries merger for text expansion in question answering

TL;DR

This paper introduces a novel method for merging multiple lexical resources into a consistent, sense-classified dictionary to enhance question answering by enriching queries and reducing noise.

Contribution

It proposes an original approach to integrate lexical data without losing consistency, enabling sense-based query expansion in QA systems.

Findings

Improved query enrichment by sense classification

Reduction of noise in lexical data integration

Demonstrated benefits for question answering accuracy

Abstract

This paper presents an original way to add new data in a reference dictionary from several other lexical resources, without loosing any consistence. This operation is carried in order to get lexical information classified by the sense of the entry. This classification makes it possible to enrich utterances (in QA: the queries) following the meaning, and to reduce noise. An analysis of the experienced problems shows the interest of this method, and insists on the points that have to be tackled.