Paper Info
Title
Automatic part of speech tagging for Arabic: an experiment using Bigram hidden Markov model
Abstract
Part Of Speech (POS) tagging is the ability to computationally determine which POS of a word is activated by its use in a particular context. POS tagger is a useful preprocessing tool in many natural languages processing (NLP) applications such as information extraction and information retrieval. In this paper, we present the preliminary achievement of Bigram Hidden Markov Model (HMM) to tackle the POS tagging problem of Arabic language. In addition, we have used different smoothing algorithms with HMM model to overcome the data sparseness problem. The Viterbi algorithm is used to assign the most probable tag to each word in the text. Furthermore, several lexical models have been defined and implemented to handle unknown word POS guessing based on word substring i.e. prefix probability, suffix probability or the linear interpolation of both of them. The average overall accuracy for this tagger is 95.8.
Year
DOI
Venue
2010
10.1007/978-3-642-16248-0_52
RSKT
Keywords
Field
DocType
suffix probability,information retrieval,hmm model,bigram hidden markov model,data sparseness problem,pos tagging problem,automatic part,unknown word pos,pos tagger,arabic language,information extraction,part of speech,natural language processing,linear interpolation,arabic languages,hidden markov model,viterbi algorithm
Substring,Computer science,Prefix,Natural language processing,Bigram,Artificial intelligence,Viterbi algorithm,Pattern recognition,Speech recognition,Natural language,Information extraction,Arabic languages,Hidden Markov model,Machine learning
Conference
Volume
ISSN
ISBN
6401
0302-9743
3-642-16247-9
Citations 
PageRank 
References 
9
0.61
9
Authors
4
Name
Order
Citations
PageRank
Mohammed Albared1383.56
Nazlia Omar27814.98
Mohd Juzaiddin Ab3719.26
Mohd Zakree Ahmad Nazri413110.52