Improved Spoken Document Retrieval With Dynamic Key Term Lexicon and Probabilistic Latent Semantic Analysis (PLSA)

Spoken document retrieval will be very important in the future network era. In this paper, we propose using a "dynamic key term lexicon" automatically extracted from the ever-changing document archives as an extra feature set in the retrieval task. This lexicon is much more compact but semantically rich, thus it can retrieve relevant documents more efficiently. The key terms include named entities and others selected by a new metric referred to as the term entropy here derived from probabilistic latent semantic analysis (PLSA). Various configurations of retrieval models were tested with a broadcast news archive in Mandarin Chinese and significant performance improvements were obtained, especially with the new version of PLSA models based on a key term lexicon rather than the full lexicon