TY - GEN
T1 - Pattern mining for information extraction using lexical, syntactic and semantic information
T2 - 4th Asia Information Retrieval Symposium, AIRS 2008
AU - Khoo, Christopher S.G.
AU - Na, Jin Cheon
AU - Wang, Wei
PY - 2008
Y1 - 2008
N2 - A method is being developed to mine a text corpus for candidate linguistic patterns for information extraction. The candidate patterns can be used to improve the quality of extraction patterns constructed by a pseudo-supervised learning method-an automated method in which the system is provided with a high quality seed pattern or clue, which is used to generate a training set automatically. The study is carried out in the context of developing a system to extract disease-treatment information from medical abstracts retrieved from the Medline database. In an earlier study, the Apriori algorithm had been used to mine a sample of sentences containing a disease concept and a drug concept, to identify frequently occurring word patterns to see if these patterns could be used to identify treatment relations in text. Word patterns and statistical association measures alone were found to be insufficient for generating good extraction patterns, and need to be combined with syntactic and semantic constraints. In this study, we explore the use of syntactic, semantic and lexical constraints to improve the quality of extraction patterns.
AB - A method is being developed to mine a text corpus for candidate linguistic patterns for information extraction. The candidate patterns can be used to improve the quality of extraction patterns constructed by a pseudo-supervised learning method-an automated method in which the system is provided with a high quality seed pattern or clue, which is used to generate a training set automatically. The study is carried out in the context of developing a system to extract disease-treatment information from medical abstracts retrieved from the Medline database. In an earlier study, the Apriori algorithm had been used to mine a sample of sentences containing a disease concept and a drug concept, to identify frequently occurring word patterns to see if these patterns could be used to identify treatment relations in text. Word patterns and statistical association measures alone were found to be insufficient for generating good extraction patterns, and need to be combined with syntactic and semantic constraints. In this study, we explore the use of syntactic, semantic and lexical constraints to improve the quality of extraction patterns.
KW - Apriori algorithm
KW - Information extraction
KW - Pattern mining
UR - http://www.scopus.com/inward/record.url?scp=45449087910&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=45449087910&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-68636-1_82
DO - 10.1007/978-3-540-68636-1_82
M3 - Conference contribution
AN - SCOPUS:45449087910
SN - 3540686339
SN - 9783540686330
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 676
EP - 681
BT - Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers
Y2 - 15 January 2008 through 18 January 2008
ER -