Hybrid Part-Of-Speech Tagger for Non-Vocalized Arabic Text

Abstract

Part of speech tagging (POS tagging) has a crucial role in different fields of natural language processing (NLP) including Speech Recognition, Natural Language Parsing, Information Retrieval and Multi Words Term Extraction. This paper proposes an efficient and accurate POS Tagging technique for Arabic language using hybrid approach. Due to the ambiguity issue, Arabic Rule-Based method suffers from misclassified and unanalyzed words. To overcome these two problems, we propose a Hidden Markov Model (HMM) integrated with Arabic Rule-Based method. Our POS tagger generates a set of three POS tags: Noun, Verb, and Particle. The proposed technique uses the different contextual information of the words with a variety of the features which are helpful to predict the various POS classes. To evaluate its accuracy, the proposed method has been trained and tested with two corpora: the Holy Quran Corpus and Kalimat Corpus for undiacritized Classical Arabic language. The experiment results demonstrate the efficiency of our method for Arabic POS Tagging. In fact, the obtained accuracies rates are 97.6%, 96.8% and 94.4% for respectively our Hybrid Tagger, HMM Tagger and for the Rule-Based Tagger with Holy Quran Corpus. And for Kalimat Corpus we obtained 94.60%, 97.40% and 98% for respectively Rule-Based Tagger, HMM Tagger and our Hybrid Tagger