Part-of-speech Tagger for Assamese Using Ensembling Approach

Abstract

Ensemble system for part-of-speech (POS) tagging is beneficial for many resource-poor languages that do not have enough annotated training data to train Deep Learning (DL, also named Deep Neural Network)-based POS taggers. An Ensemble system is a better choice to incorporate the linguistic features of a language and leverage the benefits of various types of POS taggers. In this work, we present our experiment of developing an ensemble tagger for Assamese, a low-resource, morphologically rich scheduled language of India, spoken by more than 15 million people. Despite the success of modern neural-network-based models in sequence tagging tasks, it has yet to receive attention in developing tasks such as POS in a resource-poor language such as Assamese. We develop a POS tagging model based on the BiLSTM-CRF architecture with a corpus of 404k tokens. We cover several word embeddings during training. Among all the experiments, the top two POS tagging models achieve tagging F1 scores of 0.746 and 0.745. We observe that the DL-based taggers are not able to achieve decent accuracy. It may be due to the inability to capture the linguistic features of the language or due to comparatively less annotated data. So, we build another POS tagger using a rule-based approach considering several morphological phenomena of the language and get an F1 score of 0.85. Subsequently, we integrate the top two DL-based taggers with the rule-based ones and develop a new POS tagger using an ensemble approach, of which we get an improved F1 score of 0.925. Performance improvement of our new ensemble POS taggers over the baseline taggers suggests that integration of the taggers combines the qualities of all taggers in the new tagger. Therefore, this study also states ensemble taggers are more suitable for highly inflectional, morphologically rich resource-poor languages.