As Chinese text is written without word boundaries, effectively recognizing Chinese words is like recognizing collocations in English, substituting characters for words and words for collocations. However, existing topical models that involve collocations have a common limitation. Instead of directly assigning a topic to a collocation, they take the topic of a word within the collocation as the topic of the whole collocation. This is unsatisfactory for topical modeling of Chinese documents. Thus, we propose a topical word-character model (TWC), which allows two distinct types of topics: word topic and character topic. We evaluated TWC both qualitatively and quantitatively to show that it is a powerful and a promising topic model.
REF proposed the Topical WordCharacter Model challenging the assumption that the topic of an n-gram is determined by the topics of composite words within the collocation.
Modeling Chinese Documents with Topical Word-Character Models
International Conference On Computational Linguistics
Computer Science