Topic Segmentation : A First Stage to Dialog-Based Information Extraction

We study the problem of topic segmentation of manually transcribed speech in order to facilitate information extraction from dialogs. Our approach is based on a combination of multi-source knowledge modeled by hidden Markov models. We experiment with different combinations of linguistic-level cues on dialogs dealing with search and rescue missions. Results show the effectiveness of multi-source knowledge.

• Type: Conference
• Year: 2001
• Where: NLPRS
• Authors: Narjès Boufaden, Guy Lapalme, Yoshua Bengio
• Keywords: Hidden Markov Models, Multi-source Knowledge, Natural Language Processing, NLPRS 2001, Topic Segmentation