Indrajit Bhattacharya
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Indrajit Bhattacharya is a person.
- See: IBM Research, Text Mining.
References
2009
- (Bhattacharya et al., 2009) ⇒ Indrajit Bhattacharya, Shantanu Godbole, Ajay Gupta, Ashish Verma, Jeff Achtermann, and Kevin English. (2009). “Enabling Analysts in Managed Services for CRM Analytics.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557136
2008
- (Bhattacharya et al., 2008) ⇒ Indrajit Bhattacharya, Shantanu Godbole, and Sachindra Joshi. (2008). “Structured entity identification and document categorization: two tasks with one joint model.” In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008) doi:10.1145/1401890.1401899
2007
- (Bhattacharya & Getoor, 2007) ⇒ Indrajit Bhattacharya, and Lise Getoor. (2007). “Collective Entity Resolution In Relational Data.” In: ACM Transactions on Knowledge Discovery from Data, 1(1) (TKDD). doi:10.1145/1217299.1217304.
2006
- (Bhattacharya & Getoor, 2006) ⇒ Indrajit Bhattacharya, and Lise Getoor. (2006). “A Latent Dirichlet Model for Unsupervised Entity Resolution.” In: Proceedings of the Sixth SIAM International Conference on Data Mining (SIAM 2006).
- (Bhattacharya et al., 2006) ⇒ Indrajit Bhattacharya, Louis Licamele, Lise Getoor. (2006). “Relational Clustering for Entity Resolution Queries.” In: Proceedings of the ICML 2006 Workshop on Statistical Relational Learning (SRL).
- ABSTRACT: The goal of entity resolution is to reconcile database references corresponding to the same real-world entities. Given the abundance of publicly available databases where entities are not resolved, we motivate the problem of quickly processing queries that require resolved entities from such ‘unclean’ databases. We first propose a cut-based relational clustering formulation for collective entity resolution. We then show how it can be performed on-the-fly by adaptively extracting and resolving those database references that are the most helpful for resolving the query. We validate our approach on two large real-world publication databases, where we show the usefulness of collective resolution and at the same time demonstrate the need for adaptive strategies for query processing. We then show how the same queries can be answered in real time using our adaptive approach while preserving the gains of collective resolution. 1.
- Presentation: http://www.cs.umd.edu/projects/srl2006/Slides/bhattacharya-srl06-poster.ppt
2004
- (Bhattacharya & Getoor, 2004a) ⇒ Indrajit Bhattacharya, and Lise Getoor. (2004). “Iterative Record Linkage for Cleaning and Integration.” In: Proceedings of the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. doi:10.1145/1008694.1008697
- (Bhattacharya & Getoor, 2004b) ⇒ Indrajit Bhattacharya, and Lise Getoor. (2004). “Deduplication and Group Detection Using Links.” In: Proceedings of 10th ACM SIGKDD Workshop on Link Analysis and Group Detection.
- (Bhattacharya et al., 2004) ⇒ Indrajit Bhattacharya, Lise Getoor, and Yoshua Bengio. (2004). “Word Sense Disambiguation using Probabilistic Models.” In: Proceedings of ACL 2004.