Extending a Broad-Coverage Parser for a General NLP Toolkit

With the rapid growth of real world applications for NLP systems, there is a genuine demand for a general toolkit from which programmers with no linguistic knowledge can build specific NLP systems. Such a toolkit should have a parser that is general enough to be used across domains, and yet accurate enough for each specific application. In this paper, we describe a parser that extends a broad-coverage parser, Minipar (Lin, 2001), with an adaptable shallow parser so as to achieve both generality and accuracy in handling domain specific NL problems. We test this parser on our corpus and the results show that the accuracy is significantly higher than a system that uses Minipar alone.

• Type: Journal
• Year: 2002
• Where: COLING
• Authors: Hassan Alam, Hua Cheng, Rachmat Hartono, Aman Kumar, Paul Llido, Crystal Nakatsu, Fuad Rahman, Yuliya Tarnikova, Timotius Tjahjadi, Che Wilcox
• Keywords: COLING 2002, COLING 2008, NLP Systems, Parser, Real World Applications