Neural POS tagging of shahmukhi by using contextualized word representations

Abstract

Part of Speech (POS) tagging has a preliminary role in building natural language processing applications. This paper presents the development and evaluation of the first POS tagged corpus along with a Bi-directional long-short memory (BiLSTM) network based POS tagger for Shahmukhi (Western Punjabi) at this scale. A balanced corpus of 0.13 million words has been annotated which contains text from 14 different text domains. A Shahmukhi POS tagset has been devised by studying the applicability of the CLE Urdu POS tagset and tagging guidelines have also been designed for annotation. A multi-step corpus evaluation process has been employed for tagged corpus including grammar-based and n-gram based consistency evaluations. The average inter-annotator agreement for all domains is 95.35% along with an average Kappa coefficient of 0.94. The performance of the BiLSTM POS tagger has been compared with the well-known language independent TreeTagger and the Stanford POS tagger. The accuracy of the tagger has been further improved by employing transfer learning by training context-free (Word2Vec) and contextualized (ELMo) word representations on a corpus of 14.9 Shahmukhi words which has been collected from World Wide Web. The tagger performed with an f-score of 96.11 and the accuracy of 96.12%. For a morphologically-rich and low-resourced language, these POS tagging results are quite promising.

Keywords

• Shahmukhi
• Punjabi
• POS tagging
• Corpus annotation
• Deep neural networks
• ELMo