A new approach for Arabic text classification using Arabic field-association terms
|A new approach for Arabic text classification using Arabic field-association terms|
|Author(s)||Atlam E.-S., Morita K., Fuketa M., Aoe J.-I.|
|Published in||Journal of the American Society for Information Science and Technology|
|Keyword(s)||Unknown (Extra: Arabic languages, Arabic texts, Domain specific, Experimental evaluation, Field association terms, k-NN classifier, Part Of Speech, Recall and precision, Text classification, Wikipedia, Information retrieval systems, Text processing)|
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A new approach for Arabic text classification using Arabic field-association terms is a 2011 journal article written in English by Atlam E.-S., Morita K., Fuketa M., Aoe J.-I. and published in Journal of the American Society for Information Science and Technology.
Field-association (FA) terms give us the knowledge to identify document fields using a limited set of discriminating terms. Although many earlier methods tried to extract automatically relevant FA terms to build a comprehensive dictionary, the problem lies in the lack of an effective method to extract automatically relevant FA terms to build a comprehensive dictionary. Moreover, all previous studies are based on FA terms in English and Japanese, and the extension of FA terms to other languages such as Arabic could benefit future research in the field. We present a new method to build a comprehensive Arabic dictionary using part-of-speech, pattern rules, and corpora in Arabic language. Experimental evaluation is carried out for various fields using 251 MB of domain-specific corpora obtained from Arabic Wikipedia dumps and Alhayah news selected average of 2,825 FA terms (single and compound) per field. From the experimental results, recall and precision are 84% and 79%, respectively. We propose amended text classification methodology based on field association terms. Our approach is compared with Nave Bayes (NB) and kNN classifiers on 5,959 documents from Wikipedia dumps and Alhayah news. The new approach achieved a precision of 80.65% followed by NB (72.79%) and kNN (36.15%).
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