TY - JOUR
T1 - Application of association rules mining to Named Entity Recognition and co-reference resolution for the Indonesian language
AU - Budi, Indra
AU - Bressan, Stéphane
PY - 2007/12
Y1 - 2007/12
N2 - In this paper, we propose a new method, association rules mining for Named Entity Recognition (NER) and co-reference resolution. The method uses several morphological and lexical features such as Pronoun Class (PC) and Name Class (NC), String Similarity (SP) and Position (P) in the text, into a vector of attributes. Applied to a corpus of newspaper in the Indonesian language, the method outperforms state-of-the-art maximum entropy method in name entity recognition and is comparable with state-of-the-art machine learning methods, decision tree, for co-reference resolution.
AB - In this paper, we propose a new method, association rules mining for Named Entity Recognition (NER) and co-reference resolution. The method uses several morphological and lexical features such as Pronoun Class (PC) and Name Class (NC), String Similarity (SP) and Position (P) in the text, into a vector of attributes. Applied to a corpus of newspaper in the Indonesian language, the method outperforms state-of-the-art maximum entropy method in name entity recognition and is comparable with state-of-the-art machine learning methods, decision tree, for co-reference resolution.
KW - Association rules
KW - Co-reference resolution
KW - Entity equivalence
KW - NER
KW - Named Entity Recognition
UR - http://www.scopus.com/inward/record.url?scp=48649098556&partnerID=8YFLogxK
U2 - 10.1504/IJBIDM.2007.016382
DO - 10.1504/IJBIDM.2007.016382
M3 - Article
AN - SCOPUS:48649098556
SN - 1743-8187
VL - 2
SP - 426
EP - 446
JO - International Journal of Business Intelligence and Data Mining
JF - International Journal of Business Intelligence and Data Mining
IS - 4
ER -