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Nanda Kambhatla
Researcher at IBM
Publications - 36
Citations - 2206
Nanda Kambhatla is an academic researcher from IBM. The author has contributed to research in topics: Dialog system & Natural language. The author has an hindex of 18, co-authored 35 publications receiving 2061 citations. Previous affiliations of Nanda Kambhatla include Oregon Health & Science University.
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Proceedings ArticleDOI
Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations
TL;DR: This work employs Maximum Entropy models to combine diverse lexical, syntactic and semantic features derived from the text to obtain competitive results in the Automatic Content Extraction (ACE) evaluation.
Proceedings ArticleDOI
A Mention-Synchronous Coreference Resolution Algorithm Based On the Bell Tree
TL;DR: A new approach for coreference resolution is proposed which uses the Bell tree to represent the search space and casts the coreference Resolution problem as finding the best path from the root of theBell tree to the leaf nodes.
ReportDOI
A Statistical Model for Multilingual Entity Detection and Tracking
Radu Florian,Hany Hassan,Abraham Ittycheriah,Hongyan Jing,Nanda Kambhatla,Xiaoqiang Luo,H. Nicolov,Salim Roukos +7 more
TL;DR: This paper presents a statistical language-independent framework for identifying and tracking named, nominal and pronominal references to entities within unrestricted text documents, and chaining them into clusters corresponding to each logical entity present in the text.
Proceedings Article
Comparative evaluation of a natural language dialog based system and a menu driven system for information access: a case study
Joyce Y. Chai,Jimmy Lin,Wlodek Zadrozny,Yiming Ye,Margo Budzikowska,Veronika Horvath,Nanda Kambhatla,Catherine G. Wolf +7 more
TL;DR: The evaluation of a natural language dialog based navigation system (HappyAssistant) that helps users access e-commerce sites to find relevant information about products and services shows that users prefer the natural language enabled navigation two to one over the menu driven navigation.
Proceedings Article
Combining Lexical, Syntactic, and Semantic Features with Maximum Entropy Models for Information Extraction
TL;DR: This work employs Maximum Entropy models to combine diverse lexical, syntactic and semantic features derived from the text to obtain competitive results in the Automatic Content Extraction (ACE) evaluation.