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.
Papers
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Proceedings ArticleDOI
New Embedded Representations and Evaluation Protocols for Inferring Transitive Relations
TL;DR: A new representation of types as hyper-rectangular regions, that generalize and improve on OE, and shows that some current protocols to evaluate transitive relation inference can be misleading, and offers a sound alternative.
Proceedings ArticleDOI
Mitigating the Effect of Out-of-Vocabulary Entity Pairs in Matrix Factorization for KB Inference.
TL;DR: This paper analyzes the varied performance of Matrix Factorization on the related tasks of relation extraction and knowledge-base completion and proposes three extensions to MF, including a TF-augmented MF model that is robust and obtains strong results across various KBI datasets.
Book ChapterDOI
Automated early leaderboard generation from comparative tables
TL;DR: In this paper, the authors propose a leaderboard discovery method based on partial orders between papers, where each individual performance edge is extracted from a table with citations to other papers, similar to match outcomes in an incomplete tournament.
Proceedings Article
User Interaction in the BANKS System.
B. Aditya,Soumen Chakrabarti,Rushi Desai,Arvind Hulgeri,Hrishikesh Karambelkar,Rupesh Nasre,Parag,Sundararajarao Sudarshan +7 more
Journal ArticleDOI
Learning to Rank in Vector Spaces and Social Networks
TL;DR: A survey of machine learning techniques to learn ranking functions for entities represented as feature vectors as well as nodes in a social network is presented in this paper, where probabilistic Bayesian and maximum-margin approaches to solving this problem, including efficient near-linear-time approximate algorithms, are presented.