2018/08/09 by Ketan Kumar Todi, Todi, Ketan Kumar, Pruthwik Mishra +3
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Text Readability and Simplification #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.1808.03175
10 pages, 2 figures, CICLING-2018
arxiv created 2018/08/09 · openalex publication_date 2018/08/09 · arxiv updated 2018/08/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
POS Tagging serves as a preliminary task for many NLP applications. Kannada is a relatively poor Indian language with very limited number of quality NLP tools available for use. An accurate and reliable POS Tagger is essential for many NLP tasks like shallow parsing, dependency parsing, sentiment analysis, named entity recognition. We present a statistical POS tagger for Kannada using different machine learning and neural network models. Our Kannada POS tagger outperforms the state-of-the-art Kannada POS tagger by 6%. Our contribution in this paper is three folds - building a generic POS Tagger, comparing the performances of different modeling techniques, exploring the use of character and word embeddings together for Kannada POS Tagging.