Exploiting Language Models to Classify Events from Twitter

Table 2

ConceptNet model. (a) List of relations. (b) Samples of extracted relations.

(a)

MotivatedByGoal; CausesDesire; WordNet/ParticipleOf; MemberOf; HasA; NotDesires; UsedFor; AtLocation; Entails; DefinedAs; InstanceOf; HasPainIntensity; ReceivesAction; SimilarTo; RelatedTo; NotHasProperty; PartOf; HasLastSubevent; TranslationOf; HasProperty; NotHasA; CapableOf; WordNet/adverbPertainsTo; NotCapableOf; LocationOfAction; SimilarSize; HasPainCharater; HasContext; NotMadeOf; HasFirstSubevent; SymbolOf; LocatedNear; NotUsedFor; ObstructedBy; Desires; DerivedFrom; HasSubevent; MadeOf; Antonym; CreatedBy; Attribute; DesireOf; IsA; Causes

(b)

MadeOf		AtLocation		MotivatedByGoal		ReceivesAction
Atomic bomb	Uranium	Nasa	United states	Fight war	Freedom	Bacteria	Kill
Computer	Silicon	Alcoa	Pittsburgh	Get drunk	Forget life	Army tank	Warfare
Gas	Oil	Tv channel	Russia	Pen	Write letter	Bread	Cook
Song	Music	Aozora bank	Japan	Join army	Defend country	Candle	Burn for light
Person	Live cell	Apartheid	Mall	Kill	Hate someone	Tomato	Squash
Light	Energy	Golden gate	Bridge	Live life	Pleasure	Tobacco	Chew
Carton	Wax paper	Art	Gallery	Sing	Performance	Supply	Store
Chocolate	Cocoa bean	Audience	Theatre	Socialize	Be popular	Ruby	Polish
Telephone	Electronics	Crab	Coastal area	Study	Concentrate	Money	Loan
Window	Glass	Handgun	Army	Visit museum	See history	Life	Save
⋯	⋯	⋯	⋯	⋯	⋯	⋯	⋯