Segmentation and detection at IBM: hybrid statistical models and two-tiered clustering
Topic detection and tracking
Subword-based approaches for spoken document retrieval
Subword-based approaches for spoken document retrieval
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
SeLeCT: a lexical cohesion based news story segmentation system
AI Communications - STAIRS 2002
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Information Sciences: an International Journal
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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We present a subword lexical chaining approach to automatic story segmentation of Chinese broadcast news (BN). Conventional lexical chains link related words with cohesion (e.g. repetition of words) and high concentration points of starting and ending chains are indicative of story boundaries. However, inevitable speech recognition errors in BN transcripts may destroy the cohesiveness of words, resulting in word match failures. We show the robustness of Chinese subwords (characters and syllables) in lexical matching in errorful ASR transcripts. This motivates us to discover story boundaries on chains formed by character and syllable n -gram units. Experimental results on the TDT2 Mandarin corpus show that chaining by character unigram exhibits the best story segmentation performance with relative F -measure improvement of 6.06% over conventional word chaining. Integrations of multi-scales (words and subwords) exhibit further improvement. For example, fusion by voting from different scales achieves an F -measure gain of 9.04% over words.