LREC 2000 - Papers

LREC 2000 2^nd International Conference on Language Resources & Evaluation

Conference Papers

Papers by paper title: A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Papers by ID number: 1-50, 51-100, 101-150, 151-200, 201-250, 251-300, 301-350, 351-377.

List of all papers and abstracts.

Previous Paper Next Paper

Title Acquisition of Linguistic Patterns for Knowledge-based Information Extraction

Authors Harabagiu Sanda M. (Department of Computer Science and Engineering, Southern Methodist University, Dallas, TX 75275-0122, U.S.A., sanda@renoir.seas.smu.edu)
Maiorano Steven J. (Department of Computer Science and Engineering, Southern Methodist University, Dallas, TX 75275-0122, U.S.A., steve@renoir.seas.smu.edu)

Keywords

Session Session WO11 - Mono-Multilingual Lexicon Acquisition and Building

Abstract In this paper we present a new method of automatic acquisition of linguistic patterns for Information Extraction, as implemented in the CICERO system. Our approach combines lexico-semantic information available from the WordNet database with collocating data extracted from training corpora. Due to the open-domain nature of the WordNet information and the immediate availability of large collections of texts, our method can be easily ported to open-domain Information Extraction.

Title	Acquisition of Linguistic Patterns for Knowledge-based Information Extraction
Authors	Harabagiu Sanda M. (Department of Computer Science and Engineering, Southern Methodist University, Dallas, TX 75275-0122, U.S.A., sanda@renoir.seas.smu.edu) Maiorano Steven J. (Department of Computer Science and Engineering, Southern Methodist University, Dallas, TX 75275-0122, U.S.A., steve@renoir.seas.smu.edu)
Keywords
Session	Session WO11 - Mono-Multilingual Lexicon Acquisition and Building
Abstract	In this paper we present a new method of automatic acquisition of linguistic patterns for Information Extraction, as implemented in the CICERO system. Our approach combines lexico-semantic information available from the WordNet database with collocating data extracted from training corpora. Due to the open-domain nature of the WordNet information and the immediate availability of large collections of texts, our method can be easily ported to open-domain Information Extraction.