| Paper: | SLP-P15.6 | ||
| Session: | Spoken Document Search, Navigation and Summarization | ||
| Time: | Thursday, May 18, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Speech and Spoken Language Processing: Speech data mining and document retrieval | ||
| Title: | Improved Spoken Document Retrieval with Dynamic Key Term Lexicon and Probabilistic Latent Semantic Analysis (PLSA) | ||
| Authors: | Ya-chao Hsieh, Yu-tsun Huang, Chien-chih Wang, Lin-shan Lee, National Taiwan University, Taiwan | ||
| Abstract: | 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. | ||
©2018 Conference Management Services, Inc. -||- email: webmaster@icassp2006.org -||- Last updated Friday, August 17, 2012