W10-09

Recommendations

Info

Sunday, June 6, 2010 9:00–9:10 Opening Session 1 Workshop Program 9:10–9:30 Machine Reading as a Process of Partial Question-Answering Peter Clark and Phil Harrison 9:30–9:50 Building an end-to-end text reading system based on a packed representation Doo Soon Kim, Ken Barker and Bruce Porter 9:50–10:10 Semantic Enrichment of Text with Background Knowledge Anselmo Peñas and Eduard Hovy 10:10–10:30 Discussion 10:30–11:00 Break Session 2 11:00–11:20 Large Scale Relation Detection Chris Welty, James Fan, David Gondek and Andrew Schlaikjer 11:20–11:40 Mining Script-Like Structures from the Web Niels Kasch and Tim Oates 11:40–12:00 Open-domain Commonsense Reasoning Using Discourse Relations from a Corpus of Weblog Stories Matthew Gerber, Andrew Gordon and Kenji Sagae 12:00–12:20 Discussion 12:20–2:00 Lunch ix
Page 1 and 2: NAACL HLT 2010 First International
Page 3: Introduction It has been a long ter
Page 7: Table of Contents Machine Reading a
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
Page 63 and 64:
tions. Can SRL-IE, our SRL based ex
Page 65 and 66:
TEXTRUNNER to handle unknown verbs
Page 67 and 68:
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
Page 73 and 74:
using cutoffs automatically derived
Page 75 and 76:
ate annotation-based based evaluati
Page 77 and 78:
Figure 8, Figure 9, and Figure 10 p
Page 79 and 80:
References E. Agichtein and L. Grav
Page 81 and 82:
is so because in most cases the set
Page 83 and 84:
3.1 Implicit Mention Models We adap
Page 85 and 86:
needed for promotion. In Figures 1(
Page 87 and 88:
strapping. In Figures 2(a)-(d), the
Page 89 and 90:
tities and finally derive a class h
Page 91 and 92:
quency component and the inverse do
Page 93 and 94:
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
Page 99 and 100:
formation with higher quality, yet
Page 101 and 102:
Figure 2: The evolution of major ma
Page 103 and 104:
3.6 Continuous Learning Early work
Page 105 and 106:
Parag Singla and Pedro Domingos. 20
Page 107 and 108:
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
Page 117 and 118:
Figure 2. The Knowledge Discovery a
Page 119 and 120:
engineer might want to explore the
Page 121 and 122:
from an internal KB and queried Pow
Page 123 and 124:
References Dat P. T. Nguyen, Yutaka
Page 125 and 126:
and characterize appropriate lexica
Page 127 and 128:
3.2 Change interpretations As discu
Page 129 and 130:
4.1 Characterizing components of th
Page 131 and 132:
a steady increase or decrease. We a
Page 133 and 134:
al., 1998). WordNet is a lexical re
Page 135 and 136:
Figure 2: The parse tree of the sen
Page 137:
as a “PERSON” and “XYZ Corpor
show all

W10-09

Create successful ePaper yourself

Delete template?

Save as template?