M
Mihai Surdeanu
Researcher at University of Arizona
Publications - 188
Citations - 15228
Mihai Surdeanu is an academic researcher from University of Arizona. The author has contributed to research in topics: Question answering & Computer science. The author has an hindex of 39, co-authored 163 publications receiving 13691 citations. Previous affiliations of Mihai Surdeanu include Pompeu Fabra University & Polytechnic University of Catalonia.
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Journal ArticleDOI
Deterministic coreference resolution based on entity-centric, precision-ranked rules
TL;DR: The two stages of the sieve-based architecture, a mention detection stage that heavily favors recall, followed by coreference sieves that are precision-oriented, offer a powerful way to achieve both high precision and high recall.
Proceedings ArticleDOI
Using Predicate-Argument Structures for Information Extraction
TL;DR: The experimental results prove the claim that accurate predicate-argument structures enable high quality IE results, and introduce a new way of automatically identifying predicate argument structures, which is central to the IE paradigm.
Proceedings Article
A Multi-Pass Sieve for Coreference Resolution
Karthik Raghunathan,Heeyoung Lee,Sudarshan Rangarajan,Nate Chambers,Mihai Surdeanu,Dan Jurafsky,Christopher D. Manning +6 more
TL;DR: This work proposes a simple coreference architecture based on a sieve that applies tiers of deterministic coreference models one at a time from highest to lowest precision, and outperforms many state-of-the-art supervised and unsupervised models on several standard corpora.
Proceedings Article
FALCON: Boosting Knowledge for Answer Engines
Sanda M. Harabagiu,Dan Moldovan,Marius Pasca,Rada Mihalcea,Mihai Surdeanu,Razvan Bunescu,Corina R. Gîrju,Vasile Rus,Paul Morarescu +8 more
TL;DR: FALCON, an answer engine that integrates different forms of syntactic, semantic and pragmatic knowledge for the goal of achieving better performance is discussed.
Journal ArticleDOI
Performance issues and error analysis in an open-domain question answering system
TL;DR: The overall performance of a state-of-the-art Question Answering system depends on the depth of natural language processing resources and the tools used for answer finding.