Experiments with POS Tagging Code-mixed Indian Social Media Text
arXiv:1610.09799
Abstract
This paper presents Centre for Development of Advanced Computing Mumbai's (CDACM) submission to the NLP Tools Contest on Part-Of-Speech (POS) Tagging For Code-mixed Indian Social Media Text (POSCMISMT) 2015 (collocated with ICON 2015). We submitted results for Hindi (hi), Bengali (bn), and Telugu (te) languages mixed with English (en). In this paper, we have described our approaches to the POS tagging techniques, we exploited for this task. Machine learning has been used to POS tag the mixed language text. For POS tagging, distributed representations of words in vector space (word2vec) for feature extraction and Log-linear models have been tried. We report our work on all three languages hi, bn, and te mixed with en.
3 Pages, Published in the Proceedings of the Tool Contest on POS Tagging for Code-mixed Indian Social Media (Facebook, Twitter, and Whatsapp) Text
References in corpus (1)
Cited by in corpus (4)
- Indic-Transformers: An Analysis of Transformer Language Models for Indian Languages
- Recurrent Neural Network based Part-of-Speech Tagger for Code-Mixed Social Media Text
- A POS Tagger for Code Mixed Indian Social Media Text - ICON-2016 NLP Tools Contest Entry from Surukam
- Development of POS tagger for English-Bengali Code-Mixed data