A Supervised Framework for Classifying Dependency Relations from Bengali Shallow Parsed Sentences

Natural Language Processing, one of the contemporary research area has adopted parsing technologies for various languages across the world for different objectives. In the present task, a new approach has been introduced for classifying the dependency parsed relations for a morphologically rich and free-phrase-ordered Indian language like Bengali. The pair of dependency parsed relations (also referred as kaarakas ‘cases’) are classified based on different features like vibhaktis (inflections), Part-of-Speech (POS), punctuation, gender, number and post-position. It is observed that the consecutive and non-consecutive occurrences of such relations play a vital role in the classification. We employed three different machine-learning classifiers, namely NaiveBayes, Sequential Minimal Optimization (SMO) and Conditional Random Field (CRF) which obtained the average F-Scores of 0.895, 0.869 and 0.697, respectively for classifying relation pairs of three primary kaarakas and one primary vibhakti relation. We have also conducted the error analysis for such primary relations using confusion matrices.