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Talks & Seminars
Long span contextual features for Text Classification and Entity Role Labelling
Dr. Sreeram Balakrishnan, IBM India Research Labs, New Delhi
Date & Time: August 3, 2006 17:00
Venue: KReSIT - SIC 301 (3rd floor)
Abstract:
At IRL we are exploring two methods of finding long span contextual features to improve the accuracy of text classifiers and classifiers for entity role labelling. The first method involves a modified version of the a-priori algorithm to mine high support context patterns directly from text, and the second involves using Inductive Logic Programming to find high support predicates that we then treat as features. I will present our results for text classification and entity role labelling and discuss the pro and cons if these approaches.
Speaker Profile:
Sreeram Balakrishnan manages the Unstructured Information and Text Analytics research group at IBM India Research Lab in New Delhi. Over the last 15 years has worked in R&D in Speech and Language Technologies for IBM Research and Motorola Labs. He holds a PhD in Information Engineering and Masters in Natural Language Processing from the University of Cambridge.
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