Attention, intentions, and the structure of discourse
Computational Linguistics
C4.5: programs for machine learning
C4.5: programs for machine learning
Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
The Theory and Practice of Discourse Parsing and Summarization
The Theory and Practice of Discourse Parsing and Summarization
Integrating prosodic and lexical cues for automatic topic segmentation
Computational Linguistics
Multi-paragraph segmentation of expository text
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Assessing prosodic and text features for segmentation of Mandarin broadcast news
SpeechIR '04 Proceedings of the Workshop on Interdisciplinary Approaches to Speech Indexing and Retrieval at HLT-NAACL 2004
Information Sciences: an International Journal
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Automatic topic segmentation, separation of a discourse stream into its constituent stories or topics, is a necessary preprocessing step for applications such as information retrieval, anaphora resolution, and summarization. While significant progress has been made in this area for text sources and for English audio sources, little work has been done in automatic, acoustic feature-based segmentation of other languages. In this paper, we focus on prosody-based topic segmentation of Mandarin Chinese. As a tone language, Mandarin presents special challenges for applicability of intonation-based techniques, since the pitch contour is also used to establish lexical identity. We demonstrate that intonational cues such as reduction in pitch and intensity at topic boundaries and increase in duration and pause still provide significant contrasts in Mandarin Chinese. We also build a decision tree classifier that, based only on word and local context prosodic information without reference to term similarity, cue phrase, or sentence-level information, achieves boundary classification accuracy of 89--95.8% on a large standard test set.