POS tagger model for Kannada text with CRF++ and deep learning approaches

Abstract

Computational Linguistics is one of the interesting topics in the research field of Computer Science. This paper presents training for Part of Speech (POS) tagging on Kannada words using two techniques. First approach is supervised machine learning technique CRF++0.50 (Conditional Random Field). The second approach is a combination of word embedding and deep learning techniques. The total dataset used for this implementation is 1200 tagged Kannada sentences downloaded from Technology Development for Indian Languages (TDIL). We divided the dataset into 1100 sentences (13,600 words) as training data and 100 sentences (1053 words) as test data. The BIS (Bureau of Indian Standards) tagset is used in this work in which 27 major POS tags have been considered. An accuracy obtained through CRF++0.50 tool is 76% and that with deep learning technique is 71%. The precision, recall and f-score of each tag using both the techniques are considerable.

Subject Classification: (2010):

• Primary 68T50
• Secondary 68T35

Keywords:

• Part of Speech tagging
• Conditional Random Field
• Deep Learning
• Word Embedding