Topic modeling for spoken document retrieval using word- and syllable-level information
Shih-Hsiang Lin, Berlin Chen · 2009
Topic modeling for information retrieval (IR) has attracted significant attention and demonstrated good performance in a wide variety of tasks over the years. In this article, we first present a comprehensive comparison among various topic modeling approaches, including the so-called document topic models (DTM) and word topic models (WTM), for Chinese spoken document retrieval (SDR). Moreover, in order to lessen SDR performance degradation when using imperfect recognition transcripts, we also leverage different levels of indexing features for topic modeling, including words, syllable-level units and their combinations. All the experiments are performed on the TDT Chinese collection.