W10-09

Recommendations

Info

wehaveintroducedinferenceproceduresthatarecapableofgeneratingopen-domaintextualinferences fromtheextracted knowledge. Ourevaluation resultssuggestmanyopportunitiesforfutureworkin thisarea. Acknowledgments The authors would like to thank the anonymous reviewers for their helpful comments and suggestions. The project or effort described here has beensponsoredbytheU.S.ArmyResearch,Development, and Engineering Command (RDECOM). Statementsandopinionsexpresseddonotnecessarily reflect the position orthe policy ofthe United States Government, and no official endorsement shouldbeinferred. References CollinBaker,CharlesFillmore,andJohnLowe. 1998. TheBerkeleyFrameNetproject. InChristianBoitet andPeteWhitelock,editors,ProceedingsoftheThirty- SixthAnnualMeetingoftheAssociationforComputationalLinguisticsandSeventeenthInternationalConference on ComputationalLinguistics, pages 86–90, SanFrancisco,California.MorganKaufmannPublishers. RoyBar-Haim,JonathanBerant,andIdoDagan. 2009. A compactforest for scalable inferenceover entailmentandparaphraserules.InProceedingsofthe2009 Conference on Empirical Methods in Natural LanguageProcessing,pages1056–1065,Singapore,August.AssociationforComputationalLinguistics. K.Burton,A.Java,andI.Soboroff. 2009. Theicwsm 2009spinn3rdataset. InProceedingsoftheThirdAnnualConferenceonWeblogsandSocialMedia. LynnCarlsonandDanielMarcu. 2001. Discoursetaggingmanual.TechnicalReportISI-TR-545,ISI,July. EugeneCharniakandMarkJohnson. 2005. Coarse-tofinen-bestparsingandmaxentdiscriminativereranking. InProceedingsofthe43rdAnnualMeetingon AssociationforComputationalLinguistics. PeterClarkandPhilHarrison.2009.Large-scaleextractionanduseofknowledgefromtext. InK-CAP’09: Proceedings of the fifth international conference on Knowledgecapture,pages153–160,NewYork,NY, USA.ACM. PeterClark,ChristianeFellbaum,JerryR.Hobbs,Phil Harrison, William R. Murray, and John Thompson. 2008. AugmentingWordNetforDeepUnderstanding ofText.InJohanBosandRodolfoDelmonte,editors, 50 SemanticsinTextProcessing.STEP2008Conference Proceedings,volume1ofResearchinComputational Semantics,pages45–57.CollegePublications. Stefan Evert, Adam Kilgarriff, andSergeSharoff, editors.2008.4thWebasCorpusWorkshopCanwebeat Google? Christiane Fellbaum. 1998. WordNet: An Electronic LexicalDatabase(Language,Speech,andCommunication).TheMITPress,May. Danilo Giampiccolo, Hoa Trang Dang, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2008. The fourthfascalrecognizingtextualentailmentchallenge. InProceedingsoftheFirstTextAnalysisConference. AndrewGordonandReid Swanson. 2008. Envisioningwithweblogs.InInternationalConferenceonNew MediaTechnology. AndrewGordonandReidSwanson. 2009. Identifying personalstoriesinmillionsofweblogentries.InThird InternationalConferenceonWeblogsandSocialMedia. Jonathan Gordon, Benjamin Van Durme, and Lenhart Schubert. 2009. Weblogsasasourceforextracting generalworldknowledge. InK-CAP’09: ProceedingsofthefifthinternationalconferenceonKnowledge capture,pages185–186,NewYork,NY,USA.ACM. A.C.Graesser,M.Singer,andT.Trabasso. 1994. Constructinginferencesduringnarrativetextcomprehension.PsychologicalReview,101:371–395. IrynaGurevychandTorstenZesch,editors. 2009. The PeoplesWebMeetsNLP:CollaborativelyConstructed SemanticResources. PaulKingsburyandMarthaPalmer.2003.Propbank:the nextleveloftreebank. InProceedingsofTreebanks andLexicalTheories. DouglasB.Lenat. 1995. Cyc: alarge-scaleinvestment inknowledgeinfrastructure. Communicationsofthe ACM,38(11):33–38. DavidMcClosky,EugeneCharniak,andMarkJohnson. 2006. Rerankingandself-trainingforparseradaptation. In ACL-44: Proceedings of the 21st InternationalConferenceonComputationalLinguisticsandthe44thannualmeetingoftheAssociationforComputationalLinguistics,pages337–344,Morristown,NJ, USA.AssociationforComputationalLinguistics. IsaacPersingandVincentNg. 2009. Semi-supervised causeidentificationfromaviationsafety reports. In ProceedingsoftheJointConferenceofthe47thAnnual Meeting of the ACL and the 4th International JointConferenceonNaturalLanguageProcessingof theAFNLP,pages843–851,Suntec,Singapore,August.AssociationforComputationalLinguistics. Rashmi Prasad, Alan Lee, Nikhil Dinesh, Eleni Miltsakaki,GeraudCampion,AravindJoshi,andBonnie
Webber. 2008. Penndiscoursetreebankversion2.0. LinguisticDataConsortium,February. KenjiSagae. 2009. Analysisofdiscoursestructurewith syntactic dependencies and data-driven shift-reduce parsing.InProceedingsofthe11thInternationalConference on Parsing Technologies (IWPT’09), pages 81–84,Paris,France,October.AssociationforComputationalLinguistics. LenhartSchubertand Matthew Tong. 2003. Extractingandevaluatinggeneralworldknowledgefromthe brown corpus. In Proceedings of the HLT-NAACL 2003workshoponTextmeaning,pages7–13,Morristown,NJ,USA.AssociationforComputationalLinguistics. ReidSwansonandAndrewGordon.2008.Sayanything: Amassivelycollaborativeopendomainstorywriting companion. InFirstInternationalConferenceonInteractiveDigitalStorytelling. 51
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 10 and 11: Sunday, June 6, 2010 (continued) Se
Page 12 and 13: "coherent", based on criteria such
Page 14 and 15: (SRL). While there are a number of
Page 16 and 17: epeat select a clause chain Cu of
Page 18 and 19: even if those words reflect somethi
Page 20 and 21: Building an end-to-end text reading
Page 22 and 23: found to be wrong or correct (by su
Page 24 and 25: Text Queue Parser 4 Summary Text Mi
Page 26 and 27: formalized procedure to attach elem
Page 28 and 29: Steve_Walsh:throw:pass Steve_Walsh:
Page 30 and 31: NVNPN 2 'person':'intercept':'pass'
Page 32 and 33: 6 Related Work To build the knowled
Page 34 and 35: Large Scale Relation Detection ∗
Page 36 and 37: 1000 900 800 700 600 500 400 300 20
Page 38 and 39: to run against a web-scale corpus a
Page 40 and 41: Relation Prec Rec F1 Tuples Seeds i
Page 42 and 43: of the pattern space for any given
Page 44 and 45: Mining Script-Like Structures from
Page 46 and 47: e1 [ enter, nsubj, {customer, John}
Page 48 and 49: To identify such pairs, the topic s
Page 50 and 51: ≈ 57% 2. More words (bold) were j
Page 52 and 53: References Sergey Brin and Lawrence
Page 54 and 55: strengthsandweaknessesofthesystemin
Page 56 and 57: Fine-grainedRSTparser CollapsedRSTp
Page 58 and 59: Inference accuracy 0.3 0.25 0.2 0.1
Page 62 and 63: Semantic Role Labeling for Open Inf
Page 64 and 65: inference. Pruning involves using a
Page 66 and 67: TEXTRUNNER SRL-IE P R F1 P R F1 Bin
Page 68 and 69: that maximizes information gain div
Page 70 and 71: References Eugene Agichtein and Lui
Page 72 and 73: 1. Patterns based on words vs. pred
Page 74 and 75: state of the art information extrac
Page 76 and 77: ence of an attack. For instance, th
Page 78 and 79: manually annotated examples, which
Page 80 and 81: Towards Learning Rules from Natural
Page 82 and 83: tions of the learner suit the obser
Page 84 and 85: Accuracy Accuracy (Aggressive−Nov
Page 86 and 87: Accuracy Accuracy 100 95 90 85 80 7
Page 88 and 89: Unsupervised techniques for discove
Page 90 and 91: to categorize them. The article on
Page 92 and 93: ized label) or the most specialized
Page 94 and 95: Similarity Function k (L=2) F (L=2)
Page 96 and 97: References Sören Auer, Christian B
Page 98 and 99: a wide spectrum of solutions to the
Page 100 and 101: tion from the Web corpus at large s
Page 102 and 103: TextRunner, Kylin, KOG, WOE, WPE).
Page 104 and 105: Doug Downey, Matthew Broadhead, and
Page 106 and 107: Analogical Dialogue Acts: Supportin
Page 108 and 109: Heat flows from one place to anothe
Page 110 and 111:
Source Text Translation* QRG-CE Tex
Page 112 and 113:
1987). We simplified the syntax of
Page 114 and 115:
Gentner, D. (1983). Structure-Mappi
Page 116 and 117:
sources can help in tasks like name
Page 118 and 119:
werset in the previous step and bas
Page 120 and 121:
we were able to construct a list of
Page 122 and 123:
found at threshold of 2. There were
Page 124 and 125:
Supporting rule-based representatio
Page 126 and 127:
the domain of the spatial argument
Page 128 and 129:
the time of the movement. This link
Page 130 and 131:
don’t seem to be plausible candid
Page 132 and 133:
PRISMATIC: Inducing Knowledge from
Page 134 and 135:
Figure 1: System Overview by a suit
Page 136 and 137:
ferent dimensions. Continuing with
Page 139:
Author Index Barbella, David, 96 Ba
show all

W10-09

Create successful ePaper yourself

Delete template?

Save as template?