Automatic Keyword Extraction from Spoken Text. A Comparison of two Lexical Resources: the EDR and WordNet

TL;DR

This paper compares the effectiveness of WordNet and EDR lexical resources in automatically extracting keywords from spoken multi-party dialogues, showing that lexical resources slightly outperform purely statistical methods.

Contribution

It provides a comparative analysis of WordNet and EDR in keyword extraction from spoken text, highlighting the benefits of lexical resources over statistical approaches.

Findings

Lexical resources improve keyword extraction performance.

WordNet and EDR outperform purely statistical methods.

Slight performance differences between WordNet and EDR.

Abstract

Lexical resources such as WordNet and the EDR electronic dictionary have been used in several NLP tasks. Probably, partly due to the fact that the EDR is not freely available, WordNet has been used far more often than the EDR. We have used both resources on the same task in order to make a comparison possible. The task is automatic assignment of keywords to multi-party dialogue episodes (i.e. thematically coherent stretches of spoken text). We show that the use of lexical resources in such a task results in slightly higher performances than the use of a purely statistically based method.