Please use this identifier to cite or link to this item: https://doi.org/10.1007/11563983_7
Title: Named Entity Recognition for the Indonesian language: Combining contextual, morphological and part-of-speech features into a knowledge engineering approach
Authors: Budi, I.
Bressan, S. 
Wahyudi, G.
Hasibuan, Z.A.
Nazief, B.A.A.
Issue Date: 2005
Source: Budi, I.,Bressan, S.,Wahyudi, G.,Hasibuan, Z.A.,Nazief, B.A.A. (2005). Named Entity Recognition for the Indonesian language: Combining contextual, morphological and part-of-speech features into a knowledge engineering approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3735 LNAI : 57-69. ScholarBank@NUS Repository. https://doi.org/10.1007/11563983_7
Abstract: We present a novel named entity recognition approach for the Indonesian language. We call the new method InNER for Indonesian Named Entity Recognition. InNER is based on a set of rules capturing the contextual, morphological, and part of speech knowledge necessary in the process of recognizing named entities in Indonesian texts. The InNER strategy is one of knowledge engineering: the domain and language specific rules are designed by expert knowledge engineers. After showing in our previous work that mined association rules can effectively recognize named entities and outperform maximum entropy methods, we needed to evaluate the potential for improvement to the rule based approach when expert crafted knowledge is used. The results are conclusive: the InNER method yields recall and precision of up to 63.43% and 71.84%, respectively. Thus, it significantly outperforms not only maximum entropy methods but also the association rule based method we had previously designed. © Springer-Verlag Berlin Heidelberg 2005.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/40558
ISBN: 3540292306
ISSN: 03029743
DOI: 10.1007/11563983_7
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