Ramisetty Rajeswara Rao

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In this paper we present our experiments in parsing Hindi. We first explored Malt and MST parsers. Considering pros of both these parsers, we developed a hybrid approach combining the output of these two parsers in an intuitive manner. We report our results on both development and test data provided in the Hindi Shared Task on Parsing at workshop on MT and(More)
The hybrid cloud infrastructure is predominantly used in both industry and academia for its reusability and scalability. In deadline-constrained applications, the estimation of application execution time is a challenging task. The factors like heterogeneity, uncertainty and provisioning time delays affect the performance of hybrid cloud environment. In this(More)
Statistical systems with high accuracy are very useful in real-world applications. If these systems can capture basic linguistic information, then the usefulness of these statistical systems improve a lot. This paper is an attempt at incorporating linguistic constraints in statistical dependency parsing. We consider a simple linguistic constraint that a(More)
Speaker indexing (tracking) is the task of recognizing the multiple speakers from the given speech signal. Speaker indexing is a pattern recognition task. Every pattern recognition task is classified into three phases namely, feature extraction, training and testing phases. In this paper, we analyse and study about the major feature extraction techniques(More)
We report our dependency parsing experiments on two Indian Languages, Telugu and Hindi. We first explore two most popular dependency parsers namely, Malt parser and MST parser. Considering pros of both these parsers, we develop a hybrid approach combining the output of these two parsers in an intuitive manner. For Hindi, we report our results on test data(More)
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