Constructing a Class-Based Lexical Dictionary using Interactive Topic Models
Kugatsu Sadamitsu, Kuniko Saito, Kenji Imamura, Yoshihiro Matsuo · 2012
This paper proposes a new method of constructing arbitrary class-based related word dictionaries on interactive topic models; we assume that each class is described by a topic.We propose a new semi-supervised method that uses the simplest topic model yielded by the standard EM algorithm; model calculation is very rapid.Furthermore our approach allows a dictionary to be modified interactively and the final dictionary has a hierarchical structure.This paper makes three contributions.First, it proposes a word-based semi-supervised topic model.Second, we apply the semi-supervised topic model to interactive learning; this approach is called the Interactive Topic Model.Third, we propose a score function; it extracts the related words that occupy the middle layer of the hierarchical structure.Experiments show that our method can appropriately retrieve the words belonging to an arbitrary class.