Indrajit Bhattacharya


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Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog
Subhasis Ghosh | Arpita Kundu | Aniket Pramanick | Indrajit Bhattacharya
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

We study the problem of schema discovery for knowledge graphs. We propose a solution where an agent engages in multi-turn dialog with an expert for this purpose. Each mini-dialog focuses on a short natural language statement, and looks to elicit the expert’s desired schema-based interpretation of that statement, taking into account possible augmentations to the schema. The overall schema evolves by performing dialog over a collection of such statements. We take into account the probability that the expert does not respond to a query, and model this probability as a function of the complexity of the query. For such mini-dialogs with response uncertainty, we propose a dialog strategy that looks to elicit the schema over as short a dialog as possible. By combining the notion of uncertainty sampling from active learning with generalized binary search, the strategy asks the query with the highest expected reduction of entropy. We show that this significantly reduces dialog complexity while engaging the expert in meaningful dialog.


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Stance Classification of Context-Dependent Claims
Roy Bar-Haim | Indrajit Bhattacharya | Francesco Dinuzzo | Amrita Saha | Noam Slonim
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Recent work has addressed the problem of detecting relevant claims for a given controversial topic. We introduce the complementary task of Claim Stance Classification, along with the first benchmark dataset for this task. We decompose this problem into: (a) open-domain target identification for topic and claim (b) sentiment classification for each target, and (c) open-domain contrast detection between the topic and the claim targets. Manual annotation of the dataset confirms the applicability and validity of our model. We describe an implementation of our model, focusing on a novel algorithm for contrast detection. Our approach achieves promising results, and is shown to outperform several baselines, which represent the common practice of applying a single, monolithic classifier for stance classification.


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Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models
Indrajit Bhattacharya | Lise Getoor | Yoshua Bengio
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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The University of Maryland Senseval-3 system descriptions
Clara Cabezas | Indrajit Bhattacharya | Philip Resnik
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text