Recurrent Neural Network based Part-of-Speech Tagger for Code-Mixed Social Media Text
It addresses a domain-specific problem of linguistic complexity in social media for multilingual users, but appears incremental as it applies an existing method to new data.
The paper tackled POS tagging for code-mixed social media text in Hindi-English, Bengali-English, and Telugu-English, proposing an RNN-LM approach and submitting results to a shared task.
This paper describes Centre for Development of Advanced Computing's (CDACM) submission to the shared task-'Tool Contest on POS tagging for Code-Mixed Indian Social Media (Facebook, Twitter, and Whatsapp) Text', collocated with ICON-2016. The shared task was to predict Part of Speech (POS) tag at word level for a given text. The code-mixed text is generated mostly on social media by multilingual users. The presence of the multilingual words, transliterations, and spelling variations make such content linguistically complex. In this paper, we propose an approach to POS tag code-mixed social media text using Recurrent Neural Network Language Model (RNN-LM) architecture. We submitted the results for Hindi-English (hi-en), Bengali-English (bn-en), and Telugu-English (te-en) code-mixed data.