Title :
Association rules mining for name entity recognition
Author :
Budi, Indra ; Bressan, Stéphane
Author_Institution :
Comput. Sci. Fac., Indonesia Univ., Jakarta, Indonesia
Abstract :
We propose a new name entity class extraction method based on association rules. We evaluate and compare the performance of our method with the state of the art maximum entropy method. We show that our method consistently yields a higher precision at a competitive level of recall. This result makes our method particularly suitable for tasks whose requirements emphasize the quality rather than the quantity of results.
Keywords :
XML; computational linguistics; data mining; digital libraries; information analysis; information retrieval; maximum entropy methods; pattern recognition; Indonesian digital library; XML; association rule mining; information retrieval; linguistic systems; maximum entropy method; name entity class extraction; name entity recognition; Association rules; Computer science; Costs; Data mining; Decision trees; Dictionaries; Entropy; Information retrieval; Text recognition; Thesauri;
Conference_Titel :
Web Information Systems Engineering, 2003. WISE 2003. Proceedings of the Fourth International Conference on
Print_ISBN :
0-7695-1999-7
DOI :
10.1109/WISE.2003.1254504