Tamil Dependency Parsing: Results Using Rule Based and Corpus Based Approaches

Very few attempts have been reported in the literature on dependency parsing for Tamil. In this paper, we report results obtained for Tamil dependency parsing with rule-based and corpus-based approaches. We designed annotation scheme partially based on Prague Dependency Treebank (PDT) and manually annotated Tamil data (about 3000 words) with dependency relations. For corpus-based approach, we used two well known parsers MaltParser and MSTParser, and for the rule-based approach, we implemented series of linguistic rules (for resolving coordination, complementation, predicate identification and so on) to build dependency structure for Tamil sentences. Our initial results show that, both rule-based and corpus-based approaches achieved the accuracy of more than 74% for the unlabeled task and more than 65% for the labeled tasks. Rule-based parsing accuracy dropped considerably when the input was tagged automatically.