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Table of Contents Machine Reading as a Process of Partial Question-Answering Peter Clark and Phil Harrison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Building an end-to-end text reading system based on a packed representation Doo Soon Kim, Ken Barker and Bruce Porter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10 Semantic Enrichment of Text with Background Knowledge Anselmo Peñas and Eduard Hovy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Large Scale Relation Detection Chris Welty, James Fan, David Gondek and Andrew Schlaikjer. . . . . . . . . . . . . . . . . . . . . . . . . . . . .24 Mining Script-Like Structures from the Web Niels Kasch and Tim Oates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Open-domain Commonsense Reasoning Using Discourse Relations from a Corpus of Weblog Stories Matthew Gerber, Andrew Gordon and Kenji Sagae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Semantic Role Labeling for Open Information Extraction Janara Christensen, Mausam, Stephen Soderland and Oren Etzioni . . . . . . . . . . . . . . . . . . . . . . . . . 52 Empirical Studies in Learning to Read Marjorie Freedman, Edward Loper, Elizabeth Boschee and Ralph Weischedel . . . . . . . . . . . . . . . 61 Learning Rules from Incomplete Examples: A Pragmatic Approach Janardhan Rao Doppa, Mohammad NasrEsfahani, Mohammad Sorower, Thomas G. Dietterich, Xiaoli Fern and Prasad Tadepalli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Unsupervised techniques for discovering ontology elements from Wikipedia article links Zareen Syed and Tim Finin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 Machine Reading at the University of Washington Hoifung Poon, Janara Christensen, Pedro Domingos, Oren Etzioni, Raphael Hoffmann, Chloe Kiddon, Thomas Lin, Xiao Ling, Mausam, Alan Ritter, Stefan Schoenmackers, Stephen Soderland, Dan Weld, Fei Wu and Congle Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Analogical Dialogue Acts: Supporting Learning by Reading Analogies David Barbella and Kenneth Forbus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 A Hybrid Approach to Unsupervised Relation Discovery Based on Linguistic Analysis and Semantic Typing Zareen Syed and Evelyne Viegas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Supporting rule-based representations with corpus-derived lexical information. Annie Zaenen, Cleo Condoravdi, Daniel Bobrow and Raphael Hoffmann . . . . . . . . . . . . . . . . . . 114 PRISMATIC: Inducing Knowledge from a Large Scale Lexicalized Relation Resource James Fan, David Ferrucci, David Gondek and Aditya Kalyanpur . . . . . . . . . . . . . . . . . . . . . . . . . 122 vii
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
Page 59 and 60:
weblogstories).Becauseourdiscoursep
Page 61 and 62:
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
Page 69 and 70:
Precision 0.0 0.2 0.4 0.6 TextRunne
Page 71 and 72:
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(
Page 87 and 88:
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
Page 95 and 96:
generated a basic cluster composed
Page 97 and 98:
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
Page 109 and 110:
tences and other sophisticated rhet
Page 111 and 112:
tences are broken up into a number
Page 113 and 114:
enefits from subsequent progress in
Page 115 and 116:
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
Page 121 and 122:
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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