W10-09

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Organizers: Rutu Mulkar-Mehta (University of Southern California) James Allen (University of Rochester) Jerry Hobbs (University of Southern California) Eduard Hovy (University of Southern California) Bernardo Magnini (Povo-Trento, Italy) Chris Manning (Stanford University) Program Committee: Ken Barker (University of Texas, Austin) Peter Clark (Boeing) Ido Dagan (Bar Ilan University) Oren Etzioni (University of Washington) David Ferrucci (IBM) Marjorie Freedman (BBN) Tom Mitchell (Carnegie Mellon University) Dan Moldovan (University of Texas at Dallas) Alessandro Moschitti (University of Trento) Sergei Nirenburg (University of Maryland, Baltimore County) Tim Oates (University of Maryland, Baltimore County) Dan Oblinger (DARPA) Ellen Riloff (University of Utah) Stuart Shapiro (SUNY Buffalo) Len Schubert (Rochester University) Ralph Weischedel (BBN) Michael Witbrock (Cyc Corp.) v
Page 1 and 2: NAACL HLT 2010 First International
Page 3: Introduction It has been a long ter
Page 9 and 10: Sunday, June 6, 2010 9:00-9:10 Open
Page 11 and 12: Machine Reading as a Process of Par
Page 13 and 14: what is already known, and using th
Page 15 and 16: 4.3 Semantic Interpretation To inte
Page 17 and 18: Topic: prophase Input Paragraph: 4
Page 19 and 20: gories and Relations. Proceedings o
Page 21 and 22: Parser WSD SI (a) single interpreta
Page 23 and 24: 3.1 Producing PG representation We
Page 25 and 26: Semantic Enrichment of Text with Ba
Page 27 and 28: Figure 1. Representation of the sen
Page 29 and 30: 4.2 Building context for instances
Page 31 and 32: NVN 98 'quarterback':'throw':'pass'
Page 33 and 34: References 1. Banko, M., Cafarella,
Page 35 and 36: do not “populate databases” by
Page 37 and 38: question answering, building concep
Page 39 and 40: 3.2 Candidate-instance matching An
Page 41 and 42: war, trade, suspend relations, depo
Page 43 and 44: valid evidence to support or refute
Page 45 and 46: tail can be be controlled through i
Page 47 and 48: to the topic presented to the docum
Page 49 and 50: The overall goal was to investigate
Page 51 and 52: Event Pairs e[sack, dobj, {the, emp
Page 53 and 54: Open-domainCommonsenseReasoningUsin
Page 55 and 56:
parsingusingastack-based, shift-red
Page 57 and 58:
moreappropriately viewedasthepurpos
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weblogstories).Becauseourdiscoursep
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Webber. 2008. Penndiscoursetreebank
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tions. Can SRL-IE, our SRL based ex
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TEXTRUNNER to handle unknown verbs
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Precision 0.0 0.4 0.8 TextRunner SR
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Precision 0.0 0.2 0.4 0.6 TextRunne
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Empirical Studies in Learning to Re
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using cutoffs automatically derived
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ate annotation-based based evaluati
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Figure 8, Figure 9, and Figure 10 p
Page 79 and 80:
References E. Agichtein and L. Grav
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is so because in most cases the set
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3.1 Implicit Mention Models We adap
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needed for promotion. In Figures 1(
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strapping. In Figures 2(a)-(d), the
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tities and finally derive a class h
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quency component and the inverse do
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No. Feature Set k P R F 1 Ranked Sl
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generated a basic cluster composed
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Machine Reading at the University o
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formation with higher quality, yet
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Figure 2: The evolution of major ma
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3.6 Continuous Learning Early work
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Parag Singla and Pedro Domingos. 20
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ognized, and a set of implied commi
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tences and other sophisticated rhet
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tences are broken up into a number
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enefits from subsequent progress in
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A Hybrid Approach to Unsupervised R
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Figure 2. The Knowledge Discovery a
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engineer might want to explore the
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from an internal KB and queried Pow
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References Dat P. T. Nguyen, Yutaka
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and characterize appropriate lexica
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3.2 Change interpretations As discu
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4.1 Characterizing components of th
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a steady increase or decrease. We a
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al., 1998). WordNet is a lexical re
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Figure 2: The parse tree of the sen
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as a “PERSON” and “XYZ Corpor
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W10-09

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