Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
Topic segmentation with an aspect hidden Markov model
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A critique and improvement of an evaluation metric for text segmentation
Computational Linguistics
Lexical cohesion computed by thesaural relations as an indicator of the structure of text
Computational Linguistics
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
Text segmentation based on similarity between words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Multi-paragraph segmentation of expository text
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Anaphora for everyone: pronominal anaphora resoluation without a parser
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Discourse segmentation of multi-party conversation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Minimum cut model for spoken lecture segmentation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
On Evaluation Methodologies for Text Segmentation Algorithms
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Annotation of adversarial and collegial social actions in discourse
LAW VI '12 Proceedings of the Sixth Linguistic Annotation Workshop
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We present a representation of documents as directed, weighted graphs, modeling the range of influence of terms within the document as well as contextually determined semantic relatedness among terms. We then show the usefulness of this kind of representation in topic segmentation. Our boundary detection algorithm uses this graph to determine topical coherence and potential topic shifts, and does not require labeled data or training of parameters. We show that this method yields improved results on both concatenated pseudo-documents and on closed-captions for television programs.