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Search & information retrieval

Improving how people discover, understand, and use information through advances in search and retrieval.

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Publication

From Personal to Shared Annotations

Cathy Marshall, A.J. Brush

CHI ’02: CHI ’02 extended abstracts on Human factors in computing systems | January 2002

ISBN: 1-58113-454-1

Publication

Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases

Kaushik Chakrabarti, Eamonn Keogh, Sharad Mehrotra, Michael Pazzani

ACM Transactions on Database Systems (TODS) | January 2002

Publication

Buying bestsellers online: A case study in Search & Searchability

Trystan Upstill, Nick Craswell, David Hawking

7th Australasian Document Computing Symposium | January 2002

7th Australasian Document Computing Symposium

\urlhttp://research.microsoft.com/users/nickcr/pubs/upstill_adcs02.pdf

Publication

Web Question Answering: Is More Always Better?

Susan Dumais, Michele Banko, Eric Brill, Jimmy Lin, Andrew Ng

Proceedings of SIGIR 2002 | January 2002

Publication

THU TREC 2002: Web Track Experiments

Min Zhang, Ruihua Song, Chuan Lin, Liang Ma, Zhe Jiang, Yijiang Jin, Yiqun Liu, Le Zhao, Shaoping Ma

The Eleventh Text Retrieval Conference (TREC 2002) | January 2002

Publication

THU TREC 2002: Novelty Track Experiments

Min Zhang, Ruihua Song, Chuan Lin, Liang Ma, Zhe Jiang, Yijiang Jin, Yiqun Liu, Le Zhao, Shaoping Ma

The Eleventh Text REtrieval Conference (TREC 2002) | January 2002

Publication

An Approach to Integrating Query Refinement in SQL

Michael Ortega-Binderberger, Kaushik Chakrabarti, Sharad Mehrotra

EDBT Conference | January 2002

EDBT Conference

Publication

The Use of External Knowledge of Factoid QA

E.H. Hovy, U. Hermjakob, Chin-Yew Lin

The 10th Text Retrieval Conference (TREC 2001) | November 2001

Project

Whistler Text-to-Speech Engine

The talking computer HAL in the 1968 film “2001-A Space Odyssey” had an almost human voice, but it was the voice of an actor, not a computer. Getting a real computer to talk like HAL…

Project

Support Vector Machines

Support vector machines are a set of algorithms that learn from data by creating models that maximize their margin of error. Support vector machines (SVMs) are a family of algorithms for classification, regression, transduction, novelty detection, and semi-supervised learning. They…

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