Abstract

In this paper we explain the identification of karaka relations in an English sentence. We explain the genesis of the problem and present two different approaches, rule based and statistical. We briefly describe about rule based and focus more on statistical approach. We process a sentence through various stages and extract features at each stage. We train our data and identify Karaka relations using Support Vector Machines (SVM). We also explain the impact of our work on Natural Language Interfaces for Database systems.

Abstract

In this paper, we discuss our efforts to annotate nominals in the Hindi Treebank with the semantic property of animacy. Although the treebank already encodes lexical information at a number of levels such as morph and part of speech, the addition of animacy information seems promising given its relevance to varied linguistic phenomena. The suggestion is based on the theoretical and computational analysis of the property of animacy in the context of anaphora resolution, syntactic parsing, verb classification and argument differentiation.

Abstract

In this paper, we discuss our efforts to annotate nominals in the Hindi Treebank with the semantic property of animacy. Although the treebank already encodes lexical information at a number of levels such as morph and part of speech, the addition of animacy information seems promising given its relevance to varied linguistic phenomena. The suggestion is based on the theoretical and computational analysis of the property of animacy in the context of anaphora resolution, syntactic parsing, verb classification and argument differentiation.