A machine-learning approach to the identification of WH gaps

Authors:
Derrick Higgins
Affiliations:
Educational Testing Service
Venue:
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
Year:
2003

Citing 3
Cited 2

A machine learning approach to modeling scope preferences

Computational Linguistics
Head-driven statistical models for natural language parsing

Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank

Computational Linguistics - Special issue on using large corpora: II

Using linguistic principles to recover empty categories

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Empty categories in Hindi dependency treebank: analysis and recovery

LAW V '11 Proceedings of the 5th Linguistic Annotation Workshop

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we pursue a multi-modular, statistical approach to WH dependencies, using a feedforward network as our modeling tool. The empirical basis of this model and the availability of performance measures for our system address deficiencies in earlier computational work on WH gaps, which require richer sources of semantic and lexical information in order to run. The statistical nature of our models allows them to be simply combined with other modules of grammar, such as a syntactic parser.