INTEGRATING TRANSITION AND GRAPH BASED DEPENDENCY PARSERS USING ENSEMBLED AND STACKING APPROACHES FOR PARSING TELUGU LANGUAGE

Malt and Maximum Spanning Tree (MST) parsers are two popular approaches as well as the base parsers in dependency parsing, and are also known as transition and graph based parsers respectively. Each parser has its own method of constructing a dependency tree. This paper describes the approaches for integrating transition and graph based parsers for parsing Telugu sentences. Combining these parsers at learning time is called Stacking and, at parsing time is called Ensembling. Stacking has two levels, level-0 and level-1. In level-0 model is trained under transition approach and generates the augmented trained data, which is used in level-1. The augmented data is then trained using graph based parser in level 1. It has shown better results when compared to the base parsers. Ensembled approach uses variations in base parsers, six variations of base parsers were built, among which four parsers are taken from transition based parsers and two parsers from graph based parsers. Majority, Attardi and Eisner are the three methods of Ensembled Approach which used for evaluating the parsing results. Majority approach has outperformed comparative with Attardi and Eisner methods. Different number of parsers are used for evaluating the performance, and obtained good results with three variations of base parsers, they are Covington projective, Covington non-projective and Non-projective second order features. Stacking and Ensembling dependency parsing methods have shown improved results when compared to transition and graph based parsers.