The nature of statistical learning theory
The nature of statistical learning theory
Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Topic segmentation with an aspect hidden Markov model
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Topic-based document segmentation with probabilistic latent semantic analysis
Proceedings of the eleventh international conference on Information and knowledge management
A critique and improvement of an evaluation metric for text segmentation
Computational Linguistics
Topic segmentation: algorithms and applications
Topic segmentation: algorithms and applications
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Advances in domain independent linear text segmentation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Intention-based segmentation: human reliability and correlation with linguistic cues
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
A statistical model for domain-independent text segmentation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Discourse segmentation of multi-party conversation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Feature-based segmentation of narrative documents
FeatureEng '05 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing
Shallow dialogue processing using machine learning algorithms (or not)
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
The CALO meeting assistant system
IEEE Transactions on Audio, Speech, and Language Processing
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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We investigate the appropriateness of using a technique based on support vector machines for identifying thematic structure of text streams. The thematic segmentation task is modeled as a binary-classification problem, where the different classes correspond to the presence or the absence of a thematic boundary. Experiments are conducted with this approach by using features based on word distributions through text. We provide empirical evidence that our approach is robust, by showing good performance on three different data sets. In particular, substantial improvement is obtained over previously published results of word-distribution based systems when evaluation is done on a corpus of recorded and transcribed multi-party dialogs.