PP-attachment disambiguation boosted by a gigantic volume of unambiguous examples

Authors:
Daisuke Kawahara;Sadao Kurohashi
Affiliations:
Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan;Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan
Venue:
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Year:
2005

Citing 17
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging

Computational Linguistics
Making large-scale support vector machine learning practical

Advances in kernel methods
Foundations of statistical natural language processing

Foundations of statistical natural language processing
Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Head-driven statistical models for natural language parsing

Head-driven statistical models for natural language parsing
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
A maximum-entropy-inspired parser

NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Statistical models for unsupervised prepositional phrase attachment

COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
A rule-based approach to prepositional phrase attachment disambiguation

COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Combining unsupervised and supervised methods for PP attachment disambiguation

COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Chunking with support vector machines

NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
An unsupervised approach to prepositional phrase attachment using contextually similar words

ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
A maximum entropy model for prepositional phrase attachment

HLT '94 Proceedings of the workshop on Human Language Technology
A weighted polynomial information gain kernel for resolving prepositional phrase attachment ambiguities with support vector machines

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A nearest-neighbor method for resolving PP-Attachment ambiguity

IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing

Quantifying constructional productivity with unseen slot members

CALC '09 Proceedings of the Workshop on Computational Approaches to Linguistic Creativity

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a PP-attachment disambiguation method based on a gigantic volume of unambiguous examples extracted from raw corpus. The unambiguous examples are utilized to acquire precise lexical preferences for PP-attachment disambiguation. Attachment decisions are made by a machine learning method that optimizes the use of the lexical preferences. Our experiments indicate that the precise lexical preferences work effectively.