Event Coreference Resolution Heng Ji (UIUC) 5. The

Incorporating Event Attribute as Features Attribute values as features: Whether the attributes of an event mention and its candidate antecedent event conflict or not; 6% absolute gain (Chen et al., 2009)<br>

Clustering Method 1: Agglomerative Clustering Basic idea: 
Start with singleton event mentions, sort them according to the occurrence in the document
Traverse through each event mention (from left to right), iteratively merge the active event mention into a prior event (largest probability higher than some threshold) or start the event mention as a new event<br>

Clustering Method 2: Spectral Graph Clustering (Chen and Ji, 2009)<br>

Spectral Graph Clustering 0.8 0.7 0.9 0.9 0.6 0.8 0.8 0.7 0.1 0.2 0.3 0.2 A B 0.3<br>

Spectral Graph Clustering (Cont’) Start with full connected graph, each edge is weighted by the coreference value

Optimize the normalized-cut criterion (Shi and Malik, 2000)



vol(A): The total weight of the edges from group A
Maximize weight of within-group coreference links
Minimize weight of between-group coreference links<br>

Performance MUC metric does not prefer clustering results with many singleton event mentions (Chen and Ji, 2009)<br>

Remaining Challenges The performance bottleneck of event coreference resolution comes from the poor performance of event mention labeling<br>

Beyond ACE Event Coreference Annotate events beyond ACE coreference definition
ACE does not identify Events as coreferents when one mention refers only to a part of the other
In ACE, the plural event mention is not coreferent with mentions of the component individual events.
ACE does not annotate:
“Three people have been convicted…Smith and Jones were found guilty of selling guns…”
“The gunman shot Smith and his son. ..The attack against Smith.”<br>

CMU Event Coref Corpus Annotate related events at the document level, including subevents.
Examples:
“drug war” (contains subevents: attacks, crackdowns, bullying…)
“attacks” (contains subevents: deaths, kidnappings, assassination, bombed…)<br>

13 Graph Decoding (Liu et al., 2018)<br>

14 Graph Decoding (Liu et al., 2018)<br>

15 Adding Event Sequencing Features Surface-Based Script Compatibility: these features capture whether two mentions are script
compatible based on the surface information, including:

Mention headword pair.
Event type pair.
Whether two event mentions appear in the same cluster in Chambers’s event schema database (Chambers and Jurafsky, 2010).
Whether the two event mentions share arguments, and the semantic frame name of the shared argument (produced by the Semafor parser (Das and Smith, 2011)).

Discourse-Based Script Compatibility: these features capture whether two event mentions are
related given the discourse context.
Dependency path between the two mentions.
Function words (words other than Noun, Verb, Adjective and Adverb) in between the two
Mentions.
The types of other event mentions between the two mentions.
The sentence distance of two event mentions.
Whether there are temporal expressions (AGM-TMP slot from a semantic parser (Tratz and
Hovy, 2011)) in the sentences of the two mentions.

Event Ordering: this feature set tries to capture the ordering of events. We use the discourse
ordering of two mentions (forward: the antecedent is the parent; backward: the antecedent is the
child), and temporal ordering produced by Caevo (Chambers et al., 2014).<br>

16 Results (Liu et al., 2018)<br>

17 Weak Supervision (Peng et al., 2016)<br>

18 Event Representation (Peng et al., 2016)<br>

19 Domain Transfer Results (Peng et al., 2016)<br>

20 Incorporating Argument Matching (Huang et al., 2019)<br>

21 Incorporating Argument Matching (Huang et al., 2019)<br>

22 Incorporating Argument Matching (Huang et al., 2019)<br>

23 Incorporating Argument Matching (Huang et al., 2019)<br>

24 Incorporating Argument Matching (Huang et al., 2019)<br>

25 Incorporating Argument Matching (Huang et al., 2019)<br>

26 Event Mention Pairwise Similarity Threshold >=0.8 They share many coreferential entity arguments
Its now clear that many of the anti-junta activists who occupied the Trade Unions House were neither burned to death nor died of smoke inhalation , but were savagely shot at point-blank range by agents and thugs who had infiltrated the building to kill as many of the occupants as possible , burn the corpses , and then slip away without notice .
The article , of course , fails to explain how many of the people inside the building were either shot or strangled to death .
But how many will have died by then ?<br>

27 Event Mention Pairwise Similarity Threshold >=0.8 They share many coreferential entity arguments
Its now clear that many of the anti-junta activists who occupied the Trade Unions House were neither burned to death nor died of smoke inhalation , but were savagely shot at point-blank range by agents and thugs who had infiltrated the building to kill as many of the occupants as possible , burn the corpses , and then slip away without notice .
( Odessa Building Fire Kills Dozens , AP )
In a piece titled Deadly Ukraine Fire Likely Sparked by Rebels , Government Says , the WSJ pushes the improbable theory that the anti-coup activists inside the building actually burned the building down themselves , a pathetic attempt to blame the victims of a ruthless government crackdown .<br>

28 Event Mention Pairwise Similarity [0.3, 0.8) HC000030E: super-event and subevent will be considered as “corefential”
Its now clear that many of the anti-junta activists who occupied the Trade Unions House were neither burned to death nor died of smoke inhalation , but were savagely shot at point-blank range by agents and thugs who had infiltrated the building to kill as many of the occupants as possible , burn the corpses , and then slip away without notice .
The article , of course , fails to explain how many of the people inside the building were either shot or strangled to death .
These same imposters were later filmed shooting handguns and automatic weapons in the direction of the building just minutes after they had switched sides .

Photos of the victims of the Odessa fire which have been circulating on the Internet have cast doubt on the official version of events
Odessa police spokesman Volodymyr Shasbliyenko told AP the fire apparently was caused by Molotov cocktails
Right sector goons started the fire by throwing Molotov cocktails through the windows
( Deadly Ukraine Fire Likely Sparked by Rebels , Wall Street Journal )
( Odessa Building Fire Kills Dozens , AP )
In a piece titled Deadly Ukraine Fire Likely Sparked by Rebels , Government Says , the WSJ pushes the improbable theory that the anti-coup activists inside the building actually burned the building down themselves , a pathetic attempt to blame the victims of a ruthless government crackdown .<br>

29 Event Mention Pairwise Similarity [0.3, 0.8) HC000030E: super-event and subevent will be considered as “corefential”
"The sloppily-executed killing-spree proves that the fire was not the result of a spontaneous clash between pro and anti-Kiev demonstrators , but a carefully planned black-op that likely involved foreign Intel agencies working hand-in-hand with the fascist junta government in Kiev .
US war planners want to draw Putin into a conflict to justify NATO expansion , block further EU-Russian economic integration , and facilitate the pivot to Asia .
Videos on Russia Today show the agents in red arm bands mingled with the pro-Russia activists , initiated a confrontation with the cops , and then quickly switched sides when the fighting broke out .<br>

30 Event Mention Pairwise Similarity Threshold [0.3, 0.8) HC000030E: a sequence of subevents will be considered as “corefential”
Nor does the author speculate on why the police stood by while people hurled themselves from windows to escape the fire or were savagely beaten by right wing extremists on the pavement in front of the building .
Its true that Washington supports Neo-Nazi extremists who burned down the Odessa Trade Unions House .
The fascist extremists burned down the tent city , chased the activists into the building , barricaded the exits , and then set the building on fire with the obvious intention of killing the people inside .<br>

31 Event Mention Pairwise Similarity Threshold [0.3, 0.8) HC000030E: a sequence of subevents will be considered as “corefential”
The rampage in Odessa is part a broader strategy to provoke Moscow into a military confrontation .
The coverage of the Odessa massacre by the western media is as bad as any in recent memory .<br>