Automatic detection of subject/object drops in Bengali
Arjun Das, Utpal Garain, Apurbalal Senapati · 2014
This paper presents a pioneering attempt for automatic detection of drops in Bengali. The dominant drops in Bengali refer to subject, object and verb drops. Bengali is a pro-drop language and pro-drops fall under subject/object drops which this research concentrates on. The detection algorithm makes use of off-the-shelf Bengali NLP tools like POS tagger, chunker and a dependency parser. Simple linguistic rules are initially applied to quickly annotate a dataset of 8,455 sentences which are then manually checked. The corrected dataset is then used to train two classifiers that classify a sentence to either one with a drop or no drop. The features previously used by other researchers have been considered. Both the classifiers show comparable overall performance. As a by-product, the current study generates another (apart from the drop-annotated dataset) useful NLP resource, i.e. classification of Bengali verbs (all morphological variants of 881 root verbs) as per their transitivity which in turn used as a feature by the classifiers.