A Hybrid Model for Part-of-Speech Tagging and its Application to Bengali

This paper describes our work on Bengali Part of Speech (POS) tagging using a corpus-based approach. There are several approaches for part of speech tagging. This paper deals with a model that uses a combination of supervised and unsupervised learning using a Hidden Markov Model (HMM). We make use of small tagged corpus and a large untagged corpus. We also make use of Morphological Analyzer. Bengali is a highly ambiguous and relatively free word order language. We have obtained an overall accuracy of 95%. Keywords--Natural Language Processing, Machine Learning and Statistical Technology .

• Type: Conference
• Year: 2004
• Where: IJIT
• Authors: Sandipan Dandapat, Sudeshna Sarkar, Anupam Basu
• Keywords: Artificial Intelligence, Bengali Part, IJIT 2004, Large Untagged Corpus, Small Tagged Corpus