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Sasha Blair-Goldensohn

dblp:64/2595 · DBLP profile ↗
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6ranked-venue papers
2as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 5 · 1 first-authorDatabases, data management, data science and information retrieval · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › question answering
definitional question answering		 0.012003
DefScriber: a hybrid system for definitional QA · SIGIR 2003
Information retrieval
question answering		 0.012003
DefScriber: a hybrid system for definitional QA · SIGIR 2003
Information retrieval › retrieval models
hybrid retrieval		 0.012003
DefScriber: a hybrid system for definitional QA · SIGIR 2003
YearPublicationVenuePosition
2010 The viability of web-derived polarity lexicons
Leonid Velikovich, Sasha Blair-Goldensohn, Kerry Hannan, Ryan T. McDonald
 HLT-NAACL2
2009 Sentiment Summarization: Evaluating and Learning User Preferences
Kevin Lerman, Sasha Blair-Goldensohn, Ryan T. McDonald
 EACL2
2007 Building and Refining Rhetorical-Semantic Relation Models
Sasha Blair-Goldensohn, Kathy McKeown, Owen Rambow
 HLT-NAACL1
2004 MEAD - A Platform for Multidocument Multilingual Text Summarization
Dragomir R. Radev, Timothy Allison, Sasha Blair-Goldensohn, John Blitzer, Arda Çelebi, Stanko Dimitrov, Elliott Drábek, Ali Hakim, Wai Lam, Danyu Liu, Jahna Otterbacher, Horacio Saggion, Simone Teufel, Michael Topper, Adam Winkel
 LREC3
2003 DefScriber: a hybrid system for definitional QA
abstract
No abstract available.
Sasha Blair-Goldensohn, Kathy McKeown, Andrew Hazen Schlaikjer
 SIGIR1
2001 Mining the Web for Answers to Natural Language Questions
abstract
The web is now becoming one of the largest information and knowledge repositories. Many large scale search engines (Google, Fast, Northern Light, etc.) have emerged to help users find information. In this paper, we study how we can effectively use these existing search engines to mine the Web and discover the "correct" answers to factual natural language questions.We propose a probabilistic algorithm called QASM (Question Answering using Statistical Models) that learns the best query paraphrase of a natural language question. We validate our approach for both local and web search engines using questions from the TREC evaluation. We also show how this algorithm can be combined with another algorithm (AnSel) to produce precise answers to natural language questions.
Dragomir R. Radev, Zhiping Zheng, Sasha Blair-Goldensohn, Weiguo Fan, John M. Prager
 CIKM4