A conditional random field based approach for high-accuracy part-of-speech tagging using language-independent features.

Part-of-speech (POS) tagging is the process of assigning tags or labels to each word of a text based on the grammatical category. It provides the ability to understand the grammatical structure of a text and plays an important role in many natural language processing tasks like syntax understanding, semantic analysis, text processing, information retrieval, machine translation, and named entity recognition. The POS tagging involves sequential nature, context dependency, and labeling of each word. Therefore it is a sequence labeling task. The challenges faced in Urdu text processing including resource scarcity, morphological richness, free word order, absence of capitalization, agglutinative nature, spelling variations, and multipurpose usage of words raise the demand for the development of machine learning automatic POS tagging systems for Urdu. Therefore, a conditional random field (CRF) based supervised POS classifier has been developed for 33 different Urdu POS categories using the language-independent features of Urdu text for the Urdu news dataset MM-POST containing 119,276 tokens of seven different domains including Entertainment, Finance, General, Health, Politics, Science and Sports. An analysis of the proposed approach is presented, proving it superior to other Urdu POS tagging research for using a simpler strategy by employing fewer word-level features as context windows together with the word length. The effective utilization of these features for the POS tagging of Urdu text resulted in the state-of-the-art performance of the CRF model, achieving an overall classification accuracy of 96.1%.