Speaker: Ashwini Vaidya
Title: Linguistic Structure Prediction: the case of light verbs in Hindi
Date: 24 Feb 2017
Time : 5:00 PM - 6:00 PM

*Abstract:*
Light verb constructions (LVC) in Hindi are highly productive. If we can distinguish a case such as *nirnay lenaa* `decision take; decide' from an ordinary verb-argument combination *kaagaz lenaa* `paper take; take (a) paper', it has been shown to aid NLP applications such as parsing (Begum et. al, 2011) and machine translation (Pal et al, 2011).  We propose an LVC identification system using language specific features for Hindi which shows an improvement over previous work (Begum et. al, 2011). To build our system, we carry out a linguistic analysis of Hindi LVCs using Hindi Treebank annotations and propose two new features that are aimed at capturing the diversity of Hindi LVCs in the corpus. We find that our model performs robustly across a diverse range of LVCs and our results underscore the importance of semantic features, which is in keeping with the findings for English. Our error analysis also demonstrates that our classifier can be used to further refine LVC annotations in the Hindi Treebank and make them more consistent across the board.

*Bio:* Ashwini Vaidya is a DST-CSRI Post-Doctoral Fellow in IIT Delhi. She has completed her PhD in Linguistics and Cognitive Science from the University of Colorado, Boulder (2014).