S
Soumen Chakrabarti
Researcher at Indian Institute of Technology Bombay
Publications - 208
Citations - 16289
Soumen Chakrabarti is an academic researcher from Indian Institute of Technology Bombay. The author has contributed to research in topics: Ranking (information retrieval) & Web page. The author has an hindex of 55, co-authored 208 publications receiving 15481 citations. Previous affiliations of Soumen Chakrabarti include University of California & Indian Institutes of Technology.
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Proceedings ArticleDOI
Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision
TL;DR: This work enhances each base factorization with two type-compatibility terms between entity-relation pairs, and combines the signals in a novel manner to achieve up to 7% MRR gains over base models, and new state-of-the-art results on several datasets.
Proceedings ArticleDOI
Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity-seeking Queries
TL;DR: A novel technique to segment a telegraphic query and assign a coarse-grained purpose to each segment: a base entity e1, a relation type r, a target entity type t2, and contextual words s is proposed.
Proceedings ArticleDOI
Learning joint query interpretation and response ranking
Uma Sawant,Soumen Chakrabarti +1 more
TL;DR: This work proposes two new, natural formulations for joint query interpretation and response ranking that exploit bidirectional flow of information between the knowledge base and the corpus, inspired by probabilistic language models and max-margin discriminative learning.
Journal ArticleDOI
Discriminative Link Prediction using Local, Community, and Global Signals
TL;DR: A novel framework for link prediction that integrates signals from node features, the existing local link neighborhood of a node pair, community-level link density, and global graph properties and is robust to a range of application requirements is proposed.
Posted Content
Learning Joint Query Interpretation and Response Ranking
Uma Sawant,Soumen Chakrabarti +1 more
TL;DR: In this paper, the authors propose two new natural formulations for joint query interpretation and response ranking that exploit bidirectional flow of information between the knowledge base and the corpus, based on max-margin discriminative learning, with latent variables representing those uncertainties.