Anup Chalamalla

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Data cleaning techniques usually rely on some quality rules to identify violating tuples, and then fix these violations using some repair algorithms. Oftentimes, the rules, which are related to the business logic, can only be defined on some target report generated by transformations over multiple data sources. This creates a situation where the violations(More)
In this paper we address the problem of extracting important (and unimportant) discourse patterns from call center conversations. Call centers provide dialog based calling-in support for customers to address their queries, requests and complaints. A Call center is the direct interface between an organization and its customers and it is important to capture(More)
Developments in semantic search technology have motivated the need for efficient and scalable entity annotation techniques. We demonstrate RAD: a tool for Rapid Annotator Development on a document collection. RAD builds on a recent approach (Ramakrishnan et al., 2006) that translates entity annotation rules into equivalent operations on the inverted index(More)
There is a growing need for task-oriented natural language dialog systems that can interact with a user to accomplish a given objective. Recent work on building task-oriented dialog systems have emphasized the need for acquiring task-specific knowledge from un-annotated conversational data. In our work we acquire task-specific knowledge by defining(More)
Protecting sensitive information while preserving the share-ability and usability of data is becoming increasingly important. In call-centers a lot of customer related sensitive information is stored in audio recordings. In this work, we address the problem of protecting sensitive information in audio recordings and speech transcripts. We present a(More)
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