Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Building a Kannada POS Tagger Using Machine Learning and Neural Network
  Models   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.

36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph