LREC 2006 - Proceedings sorted by papers

Title	Automated Deep Lexical Acquisition for Robust Open Texts Processing
Authors	Y. Zhang, V. Kordoni
Abstract	In this paper, we report on methods to detect and repair lexical errors for deep grammars. The lack of coverage has for long been the major problem for deep processing. The existence of various errors in the hand-crafted large grammars prevents their usage in real applications. The manual detection and repair of errors requires asignificant amount of human effort. An experiment with the British National Corpus shows about 70% of the sentences contain unknownword(s) for the English Resource Grammar. With the help of error mining methods, many lexical errors are discovered, which cause a large part of the parsing failures. Moreover, with a lexical type predictor based on a maximum entropy model, new lexical entries are automatically generated. The contribution of various features for the model is evaluated. With the disambiguated full parsing results, the precision of the predictor is enhanced significantly.
Keywords	deep lexical acquisition, robust parsing, lexicon extension, error mining
Full paper	Automated Deep Lexical Acquisition for Robust Open Texts Processing

Title

Automated Deep Lexical Acquisition for Robust Open Texts Processing

Authors

Y. Zhang, V. Kordoni

Abstract

In this paper, we report on methods to detect and repair lexical errors for deep grammars. The lack of coverage has for long been the major problem for deep processing. The existence of various errors in the hand-crafted large grammars prevents their usage in real applications. The manual detection and repair of errors requires asignificant amount of human effort. An experiment with the British National Corpus shows about 70% of the sentences contain unknownword(s) for the English Resource Grammar. With the help of error mining methods, many lexical errors are discovered, which cause a large part of the parsing failures. Moreover, with a lexical type predictor based on a maximum entropy model, new lexical entries are automatically generated. The contribution of various features for the model is evaluated. With the disambiguated full parsing results, the precision of the predictor is enhanced significantly.

Keywords

deep lexical acquisition, robust parsing, lexicon extension, error mining

Full paper

Automated Deep Lexical Acquisition for Robust Open Texts Processing