Topic Label Extraction Based on Seed Words
Kou Wanqi · Zhongwen xinxi xuebao · 2013
Traditional topic models use word probability distribution to represent topics.These words are difficult to be understandable and express a consistent meaning.This paper proposed a topic label extraction method based on seed words.The method first extracts topic seed words according to weight formulas,then uses bootstrapping algorithm to generate a key phrase set that contains seed words.Finally,the method selects topic label from the key phrase set according to the integrity and generalization of a phrase.The experiments were made on two corpora.One is topic oriented reports,the other is event based news reports.According to the experimental results,the method work well in extracting a meaningful phrase to represent a topic.