Can Distributed Word Embeddings be an alternative to costly linguistic features: A Study on Parsing Hindi

Word Embeddings have shown to be useful in wide range of NLP tasks. We explore the methods of using the embeddings in Dependency Parsing of Hindi, a MoR-FWO (morphologically rich, relatively freer word order) language and show that they not only help improve the quality of parsing, but can even act as a cheap alternative to the traditional features which are costly to acquire. We demonstrate that if we use distributed representation of lexical items instead of features produced by costly tools such as Morphological Analyzer, we get competitive results. This implies that only mono-lingual corpus will suffice to produce good accuracy in case of resource poor languages for which these tools are unavailable. We also explored the importance of these representations for domain adaptation.