Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Computational Linguistics - Special issue on tense and aspect
Procedure for quantitatively comparing the syntactic coverage of English grammars
HLT '91 Proceedings of the workshop on Speech and Natural Language
The Theory and Practice of Discourse Parsing and Summarization
The Theory and Practice of Discourse Parsing and Summarization
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
Lexical cohesion computed by thesaural relations as an indicator of the structure of text
Computational Linguistics
Applied morphological processing of English
Natural Language Engineering
Discourse deixis: reference to discourse segments
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Sentence level discourse parsing using syntactic and lexical information
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Incremental parsing models for dialog task structure
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
| Hi-index | 0.00 |
We propose a novel method to predict the interparagraph discourse structure of text, i.e. to infer which paragraphs are related to each other and form larger segments on a higher level. Our method combines a clustering algorithm with a model of segment "relatedness" acquired in a machine learning step. The model integrates information from a variety of sources, such as word co-occurrence, lexical chains, cue phrases, punctuation, and tense. Our method outperforms an approach that relies on word co-occurrence alone.