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)
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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