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Fine-grainedRSTparser CollapsedRSTparser Accuracymetric causes precedes overall causes precedes overall Segmentation 36.08 44.20 36.67 44.36 30.13 43.10 Argumentidentification 25.00 33.33 25.86 26.15 23.08 25.87 Argumentclassification 66.15 50.00 64.00 79.41 83.33 79.23 Complete 22.20 28.88 22.68 31.26 21.21 30.37 ledustousethecollapsedversionoftheparserin allsubsequentexperiments. Having developed and evaluated the discourse parser, weconducted afull discourse parse of the storycorpus,whichcomprisesmorethan25million sentencessplitintonearly1millionweblogentries. Thediscourseparserextracted2.2millioninstances ofthecausesrelationand220,000instancesofthe precedes relation. Asafinalstep, weindexedthe extracteddiscourserelationswiththeLuceneinformationretrievalengine. 4 Eachdiscourseunit(two perdiscourse relation) istreated asasingle document, allowing us to query the extracted relations usinginformationretrievaltechniquesimplemented intheLucenetoolkit. 4 Generatingtextualinferences As mentioned previously, Gordon and Swanson(2008)citethreeobstaclestoperformingcommonsensereasoningusingweblogstories. Gordon andSwanson(20<strong>09</strong>)addressed thefirst(storycollection). Weaddressedthesecond(storyanalysis) bydevelopingadiscourseparsercapableofextractingcausalandtemporalrelationsfromweblogtext (Section 3). Inthissection, wepresent apreliminarysolutiontothethirdproblem-reasoningwith theextractedknowledge. 4.1 Inferencemethod Ingeneral,werequireaninferencemethodthattakes asinputthefollowingthings: Table1:RSTparserevaluation.Allvaluesarepercentages. 1. Adescriptionofthestateoreventofinterest. Thisisafree-textdescriptionofanylength. 2. Thetypeofinferencetoperform,eithercausal ortemporal. 4 Availableathttp://lucene.apache.org 46 3. Theinferencedirection,eitherforwardorbackward. Forwardcausal inference produces the effectsofthegivenstateorevent. Backward causalinferenceproducescausesofthegiven state or event. Similarly, forward and backwardtemporal inferences produce subsequent andprecedingstatesandevents,respectively. Asasimplebaselineapproach,weimplementedthe followingprocedure.First,givenatextualinputdescription d,wequerytheextracteddiscourseunits usingLucene’smodifiedversionofthevectorspace modeloverTF-IDFtermweights. Thisproducesa rankedlist Rdofdiscourseunitsmatchingtheinput description d.Wethenfilter Rd,removingdiscourse unitsthatarenotlinkedtootherdiscourseunitsby thegivenrelationandinthegivendirection.Eachelementofthefiltered Rdisthuslinkedtoadiscourse unitthatcouldpotentially satisfy theinference request.Todemonstrate,weperformforwardcausalinferenceusingthefollowinginputdescription d: (3) Johntraveledtheworld. Below,welistthethreetop-rankeddiscourseunits thatmatched d(left-handside)andtheirassociated consequents(right-handside): 1. travelingtheworld →tomurder 2. travelingfromaroundtheworldtobethere → eventhoughthiscrowdwasinternational 3. traveledacrosstheworld →toexperienceit Inanaïve way, one mightsimply choose the toprankedclausein Rdandselectitsassociatedclause astheanswertotheinferencerequest; however,in theexampleabove,thiswouldincorrectlygenerate “tomurder”astheeffectofJohn’straveling(thisis
moreappropriately viewedasthepurpose oftraveling). The other effect clauses also appear to be incorrect. Thisshouldnotcomeasmuchofasurprisebecausetherankingwasgeneratedsoleyfrom thematchscorebetweentheinputdescription and thecausesin Rd,whicharequiterelevant. One potential problem with the naïve selection method is that it ignores information contained in therankedlist R ′ d ofclausesthatareassociatedwith the clauses in Rd. In our experiments, we often observedredundancies in R ′ d thatcapturedgeneral propertiesofthedesiredinference. Intuitively,contentthatissharedacrosselementsof R ′ d couldrepresentthecoremeaningofthedesiredinferenceresult. In what follows, we describe various re-rankings of R ′ d using this shared content. For each model described, thefinalinferencepredictionisthetoprankedelementof R ′ d . Centroidsimilarity To approximate the shared contentofdiscourseunitsin R ′ d ,wetreateach discourseunitasavectorofTFscores.Wethen computetheaveragevectorandre-rankalldiscourseunitsin R ′ d basedontheircosinesimilaritywiththeaveragevector.Thisfavorsinferenceresultsthat“agree”withmanyalternative hypotheses. Descriptionscorescaling Inthisapproach,weincorporatethescorefrom Rd intothecentroid similarityscore,multiplyingthetwoandgiving equalweighttoeach. Thiscaptures theintuitionthatthetop-rankedelementof R ′ d should represent the general content of the list but shouldalsobelinkedtoanelementof Rdthat bearshighsimilaritytothegivenstateorevent description d. Log-lengthscaling When working with the centroid similarity score, weoften observed topranked elements of R ′ d that were only a few words in length. This was typically the case when components from sparse TF vectors in R ′ d matched well with components from the centroid vector. Ideally, wewould like more lengthy (but not too long) descriptions. To achievethis, wemultiplied thecentroid similarityscorebythelogarithmofthewordlength ofthediscourseunitin R ′ d . 47 Descriptionscore/log-lengthscaling In this approach,wecombinethedescriptionscorescalingandlog-lengthscaling,multiplyingthecentroidsimilaritybybothandgivingequalweight toallthreefactors. 4.2 Evaluatingthegeneratedtextualinferences To evaluate the inference re-ranking models described above, we automatically generated forward/backward causal andtemporal inferences for five documents (265 sentences) drawn randomly from the story corpus. For simplicity, we generated an inference for each sentence in each document. Each inference re-ranking model is able to generatefourtextualinferences(forward/backward causal/temporal) foreachsentence. Inourexperiments,weonlykeptthehighest-scoringofthefour inferencesgeneratedbyamodel.Oneoftheauthors thenmanuallyevaluatedthefinalpredictionsforcorrectness. Thiswasasubjectiveprocess,butitwas guidedbythefollowingrequirements: 1. Thegeneratedinferencemustincreasethelocalcoherenceofthedocument. Asdescribed byGraesseretal.(1994),readersaretypically requiredtomakeinferencesaboutthetextthat leadtoacoherent understanding thereof. We requiredthegeneratedinferencestoaidinthis task. 2. The generated inferences must be globally valid. Todemonstrateglobalvalidity,consider thefollowingactualoutput: (4) Ididn’tevenneedajacket(untilIgot there). In Example 4, the system-generated forward temporal inference is shown in parentheses. Theinferencemakessensegivenitslocalcontext; however, it is clear from the surroundingdiscourse(notshown)thatajacketwasnot neededatanypointintime(ithappenedtobe awarmday). Asaresult,thispredictionwas taggedasincorrect. Table 2 presents the results of the evaluation. As showninthetable, thetop-performing modelsare thosethatcombinecentroid similarity withoneor bothoftheotherre-rankingheuristics.
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 58 and 59: Inference accuracy 0.3 0.25 0.2 0.1
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
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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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