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Inference accuracy 0.3 0.25 0.2 0.15 0.1 0.05 Re-rankingmodel Inferenceaccuracy(%) None 10.19 Centroidsimilarity 12.83 Descriptionscorescaling 17.36 Log-lengthscaling 12.83 Descriptionscore/log-lengthscaling 16.60 0 0.2 0.05 0.1 0.15 Table2:Inferencegenerationevaluationresults. 0.4 0.25 0.3 0.35 0.8 0.45 0.5 0.55 0.6 0.65 0.7 0.75 Confidence-ordered percentage of all inferences 0.85 0.9 0.95 1 None Centroid similarity Description score scaling Log-length scaling Combined scaling Figure1: Inferencerateversusaccuracy. Valuesalongthex-axisindicatethatthetop-scoringx%ofallinferences wereevaluated.Valuesalongthey-axisindicatethepredictionaccuracy. Theanalysisabovedemonstratestherelativeperformanceofthemodelswhenmakinginferencesfor allsentences; howeveritisprobably thecase that manygeneratedinferencesshouldberejecteddueto theirlowscore.Becausetheoutputscoresofasingle modelcanbemeaningfullycomparedacrosspredictions,itispossibletoimposeathresholdontheinferencegenerationprocesssuchthatanyprediction scoring atorbelowthethreshold iswithheld. We variedthepredictionthresholdfromzerotoavalue sufficientlylargethatitexcludedallpredictionsfor amodel. Doingsodemonstrates the trade-off betweenmakingalargenumberoftextualinferences and making accurate textual inferences. Figure 1 showstheeffectsofthisvariableonthere-ranking models. Asshown in Figure 1, the highest inferenceaccuracyisreachedbythere-rankerthatcombinesdescriptionscoreandlog-lengthscalingwiththecentroidsimilaritymeasure.Thisaccuracyisat- 48 tainedbykeepingthetop25%mostconfidentinferences. 5 Conclusions We have presented an approach to commonsense reasoningthatrelieson(1)theavailabilityofalarge corpusofpersonalweblogstoriesand(2)theabilitytoanalyzeandperforminferencewiththesestories.Ourcurrentresults,althoughpreliminary,suggestnovelandimportantareasoffutureexploration. Wegroupourobservationsaccordingtothelasttwo problemsidentifiedbyGordonandSwanson(2008): storyanalysisandenvisioningwiththeanalysisresults. 5.1 Storyanalysis AsinotherNLPtasks,weobservedsignificantperformancedegradationwhenmovingfromthetraininggenre(newswire)tothetestinggenre(Internet
weblogstories).Becauseourdiscourseparserrelies heavilyonlexicalandsyntacticfeaturesforclassification,andbecausethedistributionofthefeature values varies widely between the two genres, the performancedegradationistobeexpected. Recent techniquesinparseradaptationfortheBrowncorpus (McCloskyetal.,2006)mightbeusefullyappliedto thewebloggenreaswell. Our supervised classification-based approach to discourseparsingcouldalsobeimprovedwithadditionaltrainingdata.Causalandtemporalrelations areinstantiatedacombined2,840timesintheRST corpus,withalargemajorityofthesebeingcausal. Incontrast,thePennDiscourseTreeBank(Prasadet al.,2008)contains7,448traininginstancesofcausal relations and 2,763 training instances of temporal relations. This represents asignificant increase in theamountoftrainingdataovertheRSTcorpus. It wouldbeinformativetocompareourcurrentresults withthoseobtainedusingadiscourseparsertrained onthePennDiscourseTreeBank. Onemightalsoextractcausalandtemporalrelationsusingtraditionalsemanticroleanalysisbased on FrameNet (Baker et al., 1998) or PropBank (KingsburyandPalmer,2003).Theformerdefinesa numberofframesrelatedtocausationandtemporal order,androleswithinthelattercouldbemappedto standardthematicroles(e.g.,cause)viaSemLink. 5 5.2 Envisioningwiththeanalysisresults Webelieve commonsense reasoning based on weblogstoriescanalsobeimprovedthroughmoresophisticatedusesoftheextracteddiscourserelations.Asafirststep,itwouldbebeneficialtoexplorealternateinputdescriptions.AspresentedinSection4.2, wemaketextualinferencesatthesentencelevelfor simplicity;however,itmightbemorereasonableto makeinferencesattheclauselevel,sinceclausesare thebasisforRSTandPennDiscourseTreeBankannotation.Thiscouldresultinthegenerationofsignificantlymoreinferencesduetomulti-clausesentences;thus,moreintelligentinferencefilteringwill berequired. Our models use prediction scores for the tasks ofrejectinginferences andselectingbetweenmultiple candidate inferences (i.e., forward/backward 5 Availableathttp://verbs.colorado.edu/semlink 49 causal/temporal). Instead of relying on prediction scoresforthesetasks,itmightbeadvantageous to firstidentifywhetherornotenvisionmentshouldbe performedforaclause,and,ifitshould,whattype anddirection ofenvisionment would bebest. For example,considerthefollowingsentence: (5) [clause1Johnwenttothestore][clause2 becausehewashungry]. Itwouldbebetter-fromalocalcoherenceperspective-toinferthecauseofthesecondclauseinstead ofthecauseofthefirst.Thisisduetothefactthata causeforthefirstclauseisexplicitlystated,whereas acauseforthesecondclauseisnot.Inferencesmade aboutthefirstclause(e.g.,thatJohnwenttothestore becausehisdogwashungry),arelikelytobeuninformativeorinconflictwithexplicitlystatedinformation. Example5raisestheimportantissueofcontext, which webelieve needs to beinvestigated further. Here,contextreferstothediscoursethatsurrounds the clause or sentence for which the system is attemptingtogenerateatextualinference.Thecontextplacesanumberofconstraintsonallowableinferences. Forexample,inadditiontocontent-based constraintsdemonstratedinExample5,thecontext limitspronoun usage, entityreferences, andtense. Violationsoftheseconstraintswillreducelocalcoherence. Finally, the story corpus, with its vast size, is likelytocontainasignificantamountofredundancy forcommoneventsandstates. Ourcentroid-based re-ranking heuristics are inspired by this redundancy, and we expect that aggregation techniques suchasclusteringmightbeofsomeusewhenapplied to the corpus as awhole. Having identified coherentclustersofcauses,itmightbeeasiertofind aconsequenceforapreviouslyunseencause. In summary, we have presented preliminary researchintothetaskofusingalarge,collaboratively constructed corpus as a commonsense knowledge repository. Ratherthanrelyingonhand-coded ontologiesandeventschemas,ourapproachrelieson the implicit knowledge contained in written naturallanguage. Wehavedemonstratedthefeasibility ofobtaining thediscourse structure ofsuchacorpus via linear-time parsing models. Furthermore,
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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 60 and 61: wehaveintroducedinferenceprocedures
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
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Heat flows from one place to anothe
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Source Text Translation* QRG-CE Tex
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1987). We simplified the syntax of
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Gentner, D. (1983). Structure-Mappi
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sources can help in tasks like name
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werset in the previous step and bas
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we were able to construct a list of
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found at threshold of 2. There were
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Supporting rule-based representatio
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the domain of the spatial argument
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the time of the movement. This link
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don’t seem to be plausible candid
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PRISMATIC: Inducing Knowledge from
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Figure 1: System Overview by a suit
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ferent dimensions. Continuing with
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Author Index Barbella, David, 96 Ba
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