Entities in Natural Language Anaphora Resolution Coreference Resolution References Coreference Resolution Hinrich Schütze and Desislava Zhekova CIS, LMU [email protected] June 21, 2013 Hinrich Schütze and Desislava Zhekova Coreference Resolution
Dec 13, 2015
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Coreference Resolution
Hinrich Schütze and Desislava Zhekova
CIS, [email protected]
June 21, 2013
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Outline
1 Entities in Natural LanguageUnderstanding Natural LanguageThe use of Entities in Natural LanguageReference Resolution
2 Anaphora ResolutionThe Task of Anaphora ResolutionTypes of Anaphora
3 Coreference ResolutionRule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Understanding Natural LanguageThe use of Entities in Natural LanguageReference Resolution
Understanding Natural Language
John: Mary baked a vanilla slice for the birthday party.
Bob: Really?
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Understanding Natural LanguageThe use of Entities in Natural LanguageReference Resolution
The use of Entities in Natural Language
John: [Mary]1 baked [a vanilla slice]2 for [the birthday party]3.
Bob: Really?
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Understanding Natural LanguageThe use of Entities in Natural LanguageReference Resolution
Reference Resolution
John: [Mary]1 baked [a vanilla slice]2 for [the birthday party]3.Unfortunately, [she]4 forgot [the cake]5 in [the oven]6.
Bob: Really?
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Understanding Natural LanguageThe use of Entities in Natural LanguageReference Resolution
Reference Resolution
# Referring Expression Referent1 Mary, she Mary2 vanilla slice, the cake the vanilla slice cake3 the birthday party the birthday party4 the oven the oven
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Understanding Natural LanguageThe use of Entities in Natural LanguageReference Resolution
Reference Resolution
Why is this helpful to NLP?
Let us ask the Natural Language Question Answering System STARTsome questions using reference:
What does the question sequence (Who is the Queen ofEngland? What is her age?) return?
What does the question sequence (Who is James Bond? Who isthe Queen of England? What is his age?) return?
What does the question sequence (Who is James Bond? Who isthe Queen of England? How old is this person?) return?
http://start.csail.mit.edu
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Understanding Natural LanguageThe use of Entities in Natural LanguageReference Resolution
Reference Resolution
The ambiguity of referring expressions is often disambiguated byhumans via clarifications questions:
Did you mean James Bond?
Did you mean the Queen?
How old is who?
Who did you mean?
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Understanding Natural LanguageThe use of Entities in Natural LanguageReference Resolution
Reference Resolution
John: [Mary]1 baked [a vanilla slice]2 for [the birthday party]3.Unfortunately, [she]4 forgot [the cake]5 in [the oven]6.
Bob: Really?
# First mention Reference1 Mary she2 vanilla slice the cake
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Understanding Natural LanguageThe use of Entities in Natural LanguageReference Resolution
Reference Resolution
antecedent - denotes the expression that appears previous to areferring expression to the same discourse entity
anaphor - denotes the referring expression to an entity that hasalready been introduced to the discourse
anaphoric relation - the relation that binds the antecedent andthe anaphor
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Understanding Natural LanguageThe use of Entities in Natural LanguageReference Resolution
Reference Resolution
Anaphora Resolution
Coreference Resolution
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
The Task of Anaphora ResolutionTypes of Anaphora
Anaphora Resolution
Anaphora Resolution (AR) - is the task that aims at the identificationof the antecedent of a target word or phrase previously introduced tothe discourse. [Mitkov, 2002]
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
The Task of Anaphora ResolutionTypes of Anaphora
Anaphora Resolution
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
The Task of Anaphora ResolutionTypes of Anaphora
Anaphora Resolution
find the correct antecedent for each anaphor
once one antecedent is found the task is complete - AR does notdetect all antecedents in the given discourse
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
The Task of Anaphora ResolutionTypes of Anaphora
Types of Anaphora
The various types of anaphora may be distinguished:
according to the form of the anaphor (e.g. pronominal anaphora,lexical noun phrase anaphora, verb/adverb anaphora, zeroanaphora)
according to the locations of the anaphor and the antecedent(e.g. intrasentential anaphora, intersentential anaphora,interdocument anaphora)
other (e.g. identity-of-reference anaphora, identity-of-senseanaphora, cataphora)
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Coreference Resolution
Coreference resolution (CR) - is the process that aims to identify thevarious referring expressions in a discourse that are associated withthe same entity and group them under the same equivalence classes.
mention/markable - potentially anaphoric phrase
coreference chain - an equivalence class or a set of mentionsrefering to the same entity
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Coreference Resolution
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Coreference Resolution
Larry King: Hello hello Jay Georgia hello.caller_3: Ah thank (you) (Larry). And (Mike) (I) loved ((your)
book). (It) was great. And toward the end of the(book) (you) said Secretary (Putin of Russia) hadasked (you) to come over and (interview) (him).Had (you) done (that)? Uh and (I)’d like to knowabout (it). Thank (you) so much.
Mike Wallace: Yeah.Larry King: (I) did interview (Putin) yes.Mike Wallace: on the sixtieth anniversary of the uh end of World
War Two (he) asked (me) to come on over and (in-terview) (him). And (it) was carried uh in a lot ofplaces. But (I) tell you something. (Putin) to (my)way of thinking who calls (himself) a democrat -(He)’s not our kind of democrat.
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Hands On
How many coreference chains do these mentions form?
Which are the chains?
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Coreference Resolution
Larry King: Hello hello Jay Georgia hello.caller_3: Ah thank (you1) (Larry1). And (Mike2) (I3) loved
((your2) book4). (It4) was great. And toward theend of the (book4) (you2) said Secretary (Putinof Russia5) had asked (you2) to come over and(interview6) (him5). Had (you2) done (that6)? Uhand (I3)’d like to know about (it6). Thank (you2) somuch.
Mike Wallace: Yeah.Larry King: (I1) did interview (Putin5) yes.Mike Wallace: on the sixtieth anniversary of the uh end of World
War Two (he5) asked (me2) to come on over and(interview7) (him5). And (it7) was carried uh in a lotof places. But (I2) tell you something. (Putin5) to(my2) way of thinking who calls (himself5) a demo-crat - (He5)’s not our kind of democrat.
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Coreference Resolution
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Reference Resolution
John: [Mary]1 baked [a vanilla]2 slice for [the birthday party]3.Unfortunately, [she]4 forgot [the cake]5 in [the oven]6.
Bob: Really?
What about mentions, such as [the birthday party]3 and [the oven]6.These entities are only introduced once, but never referred to!
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Coreference Resolution
singletons - mentions that refer to an entity in the text that no othermention refers to
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Coreference Resolution
So, how do we identify coreferential relations? Similar to the WSD taskthat we previously discussed, we have two different approaches:
rule-based approaches
machine learning approaches
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Rule-based CR
Rule-based approaches rely on:
the availability of lexical and encyclopedic knowledge
manually handcrafted rules
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Rule-based CR
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Rule-based CR
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Machine Learning for CR
Machine Learning for CR tries to meet the drawbacks of rule-basedapproaches:
the cost for manually developing rules
the cost for maintaining and extending the rules
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Machine Learning for CR
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Coreference Models
Coreference Resolution is often represented as a binary classificationtask and there are several CR models that can be used for thispurpose [Rahman and Ng, 2011]:
mention-pair model
mention-ranking model
entity-mention model
cluster-ranking model
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Evaluation
Most widely used evaluation metrics are: MUC, B3, both CEAFvariants (CEAFe and CEAFm) and BLANC. None of them, however,manages to provide an optimal evaluation. This is well demonstratedby the two baselines (singletons and all-in-one).
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Baselines
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Baselines
MUC CEAF B3 BLANCR P F1 R P F1 R P F1 R P BLANC
SINGLETONS 0.0 0.0 0.0 71.2 71.2 71.2 71.2 100 83.2 50.0 49.2 49.6ALL-IN-ONE 100 29.2 45.2 10.5 10.5 10.5 100 3.5 6.7 50.0 0.8 1.6
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Evaluation Settings
gold-closed - gold linguistic annotations must be used by the systemsand no external tools and resources are allowed for additionalpreprocessing.
auto-closed - auto linguistic annotations must be used by the systemsand no external tools and resources are allowed for additionalpreprocessing.
gold-open - gold linguistic annotations must be used by the systemsand external tools and resources are allowed for additionalpreprocessing.
auto-open - auto linguistic annotations must be used by the systemsand external tools and resources are allowed for additionalpreprocessing.
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
DataWord# Word POS ParseBit PredLemma PFID WS SA NE PredArgs PredArgs Coref0 It PRP (TOP(S(NP*) - - - Speaker#1 * * (ARG1*) (22)1 is VBZ (VP* - 03 - Speaker#1 * (V*) * -2 composed VBN (VP* - 01 2 Speaker#1 * * (V*) -3 of IN (PP* - - - Speaker#1 * * (ARG2* -4 a DT (NP(NP* - - - Speaker#1 * * * (245 primary JJ * - - - Speaker#1 * * * -6 stele NN *) - - - Speaker#1 * * * 24)7 , , * - - - Speaker#1 * * * -8 secondary JJ (NP* - - - Speaker#1 * * * (139 steles NNS *) - - - Speaker#1 * * * 13)10 , , * - - - Speaker#1 * * * -11 a DT (NP* - - - Speaker#1 * * * -12 huge JJ * - - - Speaker#1 * * * -13 round NN * - - - Speaker#1 * * * -14 sculpture NN (NML(NML*) - - - Speaker#1 * * * -15 and CC * - - - Speaker#1 * * * -16 beacon NN (NML* - - - Speaker#1 * * * -17 tower NN *))) - - - Speaker#1 * * * -18 , , * - - - Speaker#1 * * * -19 and CC * - - - Speaker#1 * * * -20 the DT (NP* - - - Speaker#1 (WORK_OF_ART* * * -21 Great NNP * - - - Speaker#1 * * * -22 Wall NNP *) - - - Speaker#1 *) * * -23 , , * - - - Speaker#1 * * * -24 among IN (PP* - - - Speaker#1 * * * -25 other JJ (NP* - - - Speaker#1 * * * -26 things NNS *)))))) - - - Speaker#1 * * *) -27 . . *)) - - - Speaker#1 * * * -
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Data
#begin document <document ID><sentence>
<sentence>...<sentence>
#end document <document ID>...#begin document <document ID><sentence>
<sentence>...<sentence>
#end document <document ID>
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Data
<token#1 column#1> <token#1 column#2> <token#1 column#3> ...<token#2 column#1> <token#2 column#2> <token#2 column#3> ...<token#3 column#1> <token#3 column#2> <token#3 column#3> ......
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
The CR pipeline
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Mention Detection
Identification of mentions using:
Heuristic approaches – POS, NEs
Rule-based approaches – syntactic annotation
Machine learning approaches – can use all types of providedannotations
Hybrid approaches – combination of implemented approaches
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Mention Detection MethodsWord# Word POS ParseBit PredLemma PFID WS SA NE PredArgs PredArgs Coref0 It PRP (TOP(S(NP*) - - - Speaker#1 * * (ARG1*) (22)1 is VBZ (VP* - 03 - Speaker#1 * (V*) * -2 composed VBN (VP* - 01 2 Speaker#1 * * (V*) -3 of IN (PP* - - - Speaker#1 * * (ARG2* -4 a DT (NP(NP* - - - Speaker#1 * * * (245 primary JJ * - - - Speaker#1 * * * -6 stele NN *) - - - Speaker#1 * * * 24)7 , , * - - - Speaker#1 * * * -8 secondary JJ (NP* - - - Speaker#1 * * * (139 steles NNS *) - - - Speaker#1 * * * 13)10 , , * - - - Speaker#1 * * * -11 a DT (NP* - - - Speaker#1 * * * -12 huge JJ * - - - Speaker#1 * * * -13 round NN * - - - Speaker#1 * * * -14 sculpture NN (NML(NML*) - - - Speaker#1 * * * -15 and CC * - - - Speaker#1 * * * -16 beacon NN (NML* - - - Speaker#1 * * * -17 tower NN *))) - - - Speaker#1 * * * -18 , , * - - - Speaker#1 * * * -19 and CC * - - - Speaker#1 * * * -20 the DT (NP* - - - Speaker#1 (WORK_OF_ART* * * -21 Great NNP * - - - Speaker#1 * * * -22 Wall NNP *) - - - Speaker#1 *) * * -23 , , * - - - Speaker#1 * * * -24 among IN (PP* - - - Speaker#1 * * * -25 other JJ (NP* - - - Speaker#1 * * * -26 things NNS *)))))) - - - Speaker#1 * * *) -27 . . *)) - - - Speaker#1 * * * -
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Using NEs
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Using NEsWord# Word POS ParseBit PredLemma PFID WS SA NE PredArgs PredArgs Coref0 It PRP (TOP(S(NP*) - - - Speaker#1 * * (ARG1*) (22)1 is VBZ (VP* - 03 - Speaker#1 * (V*) * -2 composed VBN (VP* - 01 2 Speaker#1 * * (V*) -3 of IN (PP* - - - Speaker#1 * * (ARG2* -4 a DT (NP(NP* - - - Speaker#1 * * * (245 primary JJ * - - - Speaker#1 * * * -6 stele NN *) - - - Speaker#1 * * * 24)7 , , * - - - Speaker#1 * * * -8 secondary JJ (NP* - - - Speaker#1 * * * (139 steles NNS *) - - - Speaker#1 * * * 13)10 , , * - - - Speaker#1 * * * -11 a DT (NP* - - - Speaker#1 * * * -12 huge JJ * - - - Speaker#1 * * * -13 round NN * - - - Speaker#1 * * * -14 sculpture NN (NML(NML*) - - - Speaker#1 * * * -15 and CC * - - - Speaker#1 * * * -16 beacon NN (NML* - - - Speaker#1 * * * -17 tower NN *))) - - - Speaker#1 * * * -18 , , * - - - Speaker#1 * * * -19 and CC * - - - Speaker#1 * * * -20 the DT (NP* - - - Speaker#1 (WORK_OF_ART* * * -21 Great NNP * - - - Speaker#1 * * * -22 Wall NNP *) - - - Speaker#1 *) * * -23 , , * - - - Speaker#1 * * * -24 among IN (PP* - - - Speaker#1 * * * -25 other JJ (NP* - - - Speaker#1 * * * -26 things NNS *)))))) - - - Speaker#1 * * *) -27 . . *)) - - - Speaker#1 * * * -
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Using POS-based chunkingWord# Word POS ParseBit PredLemma PFID WS SA NE PredArgs PredArgs Coref0 It PRP (TOP(S(NP*) - - - Speaker#1 * * (ARG1*) (22)1 is VBZ (VP* - 03 - Speaker#1 * (V*) * -2 composed VBN (VP* - 01 2 Speaker#1 * * (V*) -3 of IN (PP* - - - Speaker#1 * * (ARG2* -4 a DT (NP(NP* - - - Speaker#1 * * * (245 primary JJ * - - - Speaker#1 * * * -6 stele NN *) - - - Speaker#1 * * * 24)7 , , * - - - Speaker#1 * * * -8 secondary JJ (NP* - - - Speaker#1 * * * (139 steles NNS *) - - - Speaker#1 * * * 13)10 , , * - - - Speaker#1 * * * -11 a DT (NP* - - - Speaker#1 * * * -12 huge JJ * - - - Speaker#1 * * * -13 round NN * - - - Speaker#1 * * * -14 sculpture NN (NML(NML*) - - - Speaker#1 * * * -15 and CC * - - - Speaker#1 * * * -16 beacon NN (NML* - - - Speaker#1 * * * -17 tower NN *))) - - - Speaker#1 * * * -18 , , * - - - Speaker#1 * * * -19 and CC * - - - Speaker#1 * * * -20 the DT (NP* - - - Speaker#1 (WORK_OF_ART* * * -21 Great NNP * - - - Speaker#1 * * * -22 Wall NNP *) - - - Speaker#1 *) * * -23 , , * - - - Speaker#1 * * * -24 among IN (PP* - - - Speaker#1 * * * -25 other JJ (NP* - - - Speaker#1 * * * -26 things NNS *)))))) - - - Speaker#1 * * *) -27 . . *)) - - - Speaker#1 * * * -
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Using the syntactic annotationWord# Word POS ParseBit PredLemma PFID WS SA NE PredArgs PredArgs Coref0 It PRP (TOP(S(NP*) - - - Speaker#1 * * (ARG1*) (22)1 is VBZ (VP* - 03 - Speaker#1 * (V*) * -2 composed VBN (VP* - 01 2 Speaker#1 * * (V*) -3 of IN (PP* - - - Speaker#1 * * (ARG2* -4 a DT (NP(NP* - - - Speaker#1 * * * (245 primary JJ * - - - Speaker#1 * * * -6 stele NN *) - - - Speaker#1 * * * 24)7 , , * - - - Speaker#1 * * * -8 secondary JJ (NP* - - - Speaker#1 * * * (139 steles NNS *) - - - Speaker#1 * * * 13)10 , , * - - - Speaker#1 * * * -11 a DT (NP* - - - Speaker#1 * * * -12 huge JJ * - - - Speaker#1 * * * -13 round NN * - - - Speaker#1 * * * -14 sculpture NN (NML(NML*) - - - Speaker#1 * * * -15 and CC * - - - Speaker#1 * * * -16 beacon NN (NML* - - - Speaker#1 * * * -17 tower NN *))) - - - Speaker#1 * * * -18 , , * - - - Speaker#1 * * * -19 and CC * - - - Speaker#1 * * * -20 the DT (NP* - - - Speaker#1 (WORK_OF_ART* * * -21 Great NNP * - - - Speaker#1 * * * -22 Wall NNP *) - - - Speaker#1 *) * * -23 , , * - - - Speaker#1 * * * -24 among IN (PP* - - - Speaker#1 * * * -25 other JJ (NP* - - - Speaker#1 * * * -26 things NNS *)))))) - - - Speaker#1 * * *) -27 . . *)) - - - Speaker#1 * * * -
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Toy Example following the Mention-Pair Model
[Mary1] had [a good idea2]. [She3] wanted to tell [John4].
[a good idea] [Mary][She] [a good idea][She] [Mary][John] [She][John] [a good idea][John] [Mary]
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Mention Head Detection
Example:
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Mention Head Detection
Mention Head Detection is generally realized via:
Heuristics
Rule-based methods
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Toy Example following the Mention-Pair Model
[Mary1] had [a good idea2]. [She3] wanted to tell [John4].
idea MaryShe ideaShe MaryJohn SheJohn ideaJohn Mary
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Example of Features Used by the Menion-Pair Model
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Example of Features Used by the Menion-Pair Model
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Toy Example following the Mention-Pair Model
[Mary1] had [a good idea2]. [She3] wanted to tell [John4].
idea Mary NN NNPShe idea NNP NNShe Mary PRP NNPJohn She NNP PRPJohn idea NNP NNJohn Mary NNP NNP
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Toy Example following the Mention-Pair Model
training: [Mary1] had [a good idea2]. [She1] wanted to tell [John4].
test: [Mary1] had [a good idea2]. [She3] wanted to tell [John4].
Training instances: Test instances:
idea Mary NN NNP F idea Mary NN NNPShe idea NNP NN F She idea NNP NNShe Mary PRP NNP T She Mary PRP NNPJohn She NNP PRP F John She NNP PRPJohn idea NNP NN F John idea NNP NNJohn Mary NNP NNP F John Mary NNP NNP
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Hands On
How would you employ semantic similarity for the task of coreferenceresolution?
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Rule-based approaches to CRMachine Learning approaches to CRSubtasks of CR
Thank you!
Hinrich Schütze and Desislava Zhekova Coreference Resolution
Entities in Natural LanguageAnaphora Resolution
Coreference ResolutionReferences
Bibliography
Ruslan Mitkov. Anaphora resolution. Studies in Language andLinguistics. Longman, 2002.
Altaf Rahman and Vincent Ng. Narrowing the Modeling Gap: aCluster-Ranking Approach to Coreference Resolution. J. Artif. Int.Res., 40(1):469–521, January 2011.
Hinrich Schütze and Desislava Zhekova Coreference Resolution