Enhancing Neural Network Based Dependency Parsing Using Morphological Information for Hindi

Abstract

In this paper, we propose a way of incorporating morphological resources for enhancing the performance of neural network based dependency parsing. We conduct our experiments in Hindi, which is a morphologically rich language. We report our results on two well known Hindi Dependency Parsing datasets. We show an improvement of both Unlabeled Attachment Score (UAS) and Labeled Attachment Score (LAS) compared to previous state-of-the art hindi dependency parsers using only word embeddings, POS tag embeddings and arc-label embeddings as features. Using morphological features, such as number, gender, person and case of words, we achieve an additional improvement of both LAS and UAS. We find that many of the erroneous sentences contain Named Entities. We propose a treatment for Named Entities which further improves both UAS and LAS of our Hindi dependency parser (The parser is available at http://www.cicling.org/2016/data/126/CICLing_126.zip).