Learning the fine-grained information status of discourse entities

Authors:
Altaf Rahman;Vincent Ng
Affiliations:
University of Texas at Dallas Richardson, TX;University of Texas at Dallas Richardson, TX
Venue:
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Year:
2012

Citing 9
Cited 1

The syntactic process

The syntactic process
The Berkeley FrameNet Project

COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Support vector machine learning for interdependent and structured output spaces

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Statistical anaphora resolution in biomedical texts

COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Learning information status of discourse entities

EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
The NXT-format Switchboard Corpus: a rich resource for investigating the syntax, semantics, pragmatics and prosody of dialogue

Language Resources and Evaluation
Stanford's multi-pass sieve coreference resolution system at the CoNLL-2011 shared task

CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Learning the information status of noun phrases in spoken dialogues

EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Identifying relations for open information extraction

EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing

Automatically acquiring fine-grained information status distinctions in German

SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Quantified Score

Hi-index	0.00

Visualization

Abstract

While information status (IS) plays a crucial role in discourse processing, there have only been a handful of attempts to automatically determine the IS of discourse entities. We examine a related but more challenging task, fine-grained IS determination, which involves classifying a discourse entity as one of 16 IS subtypes. We investigate the use of rich knowledge sources for this task in combination with a rule-based approach and a learning-based approach. In experiments with a set of Switchboard dialogues, the learning-based approach achieves an accuracy of 78.7%, outperforming the rule-based approach by 21.3%.