Part-of-speech tagging for Nagamese Language using CRF
This work addresses the lack of NLP resources for Nagamese, a low-resource language, by providing the first part-of-speech tagging system, which is incremental as it applies an existing method to new data.
This paper tackled part-of-speech tagging for the Nagamese language, achieving an overall accuracy of 85.70% with precision, recall, and F1-scores around 85-86% using Conditional Random Fields on an annotated corpus of 16,112 tokens.
This paper investigates part-of-speech tagging, an important task in Natural Language Processing (NLP) for the Nagamese language. The Nagamese language, a.k.a. Naga Pidgin, is an Assamese-lexified Creole language developed primarily as a means of communication in trade between the Nagas and people from Assam in northeast India. A substantial amount of work in part-of-speech-tagging has been done for resource-rich languages like English, Hindi, etc. However, no work has been done in the Nagamese language. To the best of our knowledge, this is the first attempt at part-of-speech tagging for the Nagamese Language. The aim of this work is to identify the part-of-speech for a given sentence in the Nagamese language. An annotated corpus of 16,112 tokens is created and applied machine learning technique known as Conditional Random Fields (CRF). Using CRF, an overall tagging accuracy of 85.70%; precision, recall of 86%, and f1-score of 85% is achieved. Keywords. Nagamese, NLP, part-of-speech, machine learning, CRF.