基于种子词汇的话题标签抽取研究

Topic Label Extraction Based on Seed Words

传统话题模型用词项概率分布表示话题,在可解释性上存在很大的不足。该文在Latent Dirichlet Allocation(LDA)的结果上提出了一种基于种子词汇的话题标签抽取方法。首先根据提出的权重计算公式抽取每个话题的种子词,然后,采用bootstrapping思想,迭代产生包含种子词汇的关键短语集合,最后根据短语的完整性和泛化度选择话题标签。该文对两会报告话题和新闻事件话题进行实验,通过结果展示和人工评测,该方法抽取的话题标签能够较准确地表达话题的语义信息。

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.