An unsupervised model for statistically determining coordinate phrase attachment

Authors:
Miriam Goldberg
Affiliations:
University of Pennsylvania
Venue:
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Year:
1999

Citing 4
Cited 7

Structural ambiguity and lexical relations

Computational Linguistics - Special issue on using large corpora: I
Building a large annotated corpus of English: the penn treebank

Computational Linguistics - Special issue on using large corpora: II
Statistical models for unsupervised prepositional phrase attachment

COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
A maximum entropy model for prepositional phrase attachment

HLT '94 Proceedings of the workshop on Human Language Technology

Using the web as an implicit training set: application to structural ambiguity resolution

HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Coordination disambiguation without any similarities

COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Towards identifying intervention arms in randomized controlled trials: Extracting coordinating constructions

Journal of Biomedical Informatics
Coordinate structure analysis with global structural constraints and alignment-based local features

ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Automatic detection of nocuous coordination ambiguities in natural language requirements

Proceedings of the IEEE/ACM international conference on Automated software engineering
The design of SREE: a prototype potential ambiguity finder for requirements specifications and lessons learned

REFSQ'13 Proceedings of the 19th international conference on Requirements Engineering: Foundation for Software Quality
Identifying non-elliptical entity mentions in a coordinated NP with ellipses

Journal of Biomedical Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper examines the use of an unsupervised statistical model for determining the attachment of ambiguous coordinate phrases (CP) of the form n1 p n2 cc n3. The model presented here is based on [AR98], an unsupervised model for determining prepositional phrase attachment. After training on unannotated 1988 Wall Street Journal text, the model performs at 72% accuracy on a development set from sections 14 through 19 of the WSJ TreeBank [MSM93].