Year: 2006
Type: conference-paper
Abstract: However large a hand-crafted wide-coverage grammar is, there are always going to be words and constructions that are not included in it and are going to cause parse failure. Due to their heterogeneous and flexible nature, Multiword Expressions (MWEs) provide an endless source of parse failures. As the number of such expressions in a speaker's lexicon is equiparable to the number of single word units (Jackendoff, 1997), one major challenge for robust natural language processing systems is to be able to deal with MWEs. In this paper we propose to semi-automatically detect MWE candidates in texts using some error mining techniques and validating them using a combination of the World Wide Web as a corpus and some statistical measures. For the remaining candidates possible lexico-syntactic types are predicted, and they are subsequently added to the grammar as new lexical entries. This approach provides a significant increase in the coverage of these expressions.
Authors: Yi Zhang, Valia Kordoni, Aline Villavicencio, Marco Idiart
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2108717659