Performance evaluation of part-of-speech tagging for bengali text
- Vol.103(2), Apr
- New York Springer 2022
- 577-589p.
In this paper, it has been proposed an approach to assess the performance of part-of-speech tagging of the Bengali text. The tagging can be viewed as a process of interpreting a syntactic category for tokens in text documents. The difficulty occurs when choosing an appropriate category of the speech part for tokens. To overcome this limitation, we proposed an effective method to carry out part of the speech tagging on 5 corpora independent of the domain. Subsequently, we performed a tagging performance assessment to verify the efficiency of our system. Our system is developed in 10 phases: initialization of the dataset, sentence boundary determination, tokenization, identification of unique tokens, part-of-speech tagging, retrieving the token and the tagged portion of the speech class, record the retrieved outcomes, query processing, performance evaluation and rank generation. Five corpora have been used for the experiment of the system. The system is successfully tagged 98.97%, 98.35%, 89.93%, 88.46% and 90.01% tokens of experimental corpora. The system has obtained excellent tagging performance for the POS category (Common Noun) compared to other POS categories. The efficiency of this system is visualized through detailed performance appraisal in 18 part-of-speech categories. The system is successfully tagged 16,504,118 tokens over 18,047,593 numbers of distinct tokens in the total corpora. It has been achieved 91.44% overall tagging effectiveness, which represents an improvement of about 3.24% compared to the baseline method.