A constraint-based hypergraph partitioning approach to ......I Linking. I Links mentions and partial...

A constraint-based hypergraph partitioningapproach to coreference resolution

Emili Sapena, Lluıs Padro, Jordi Turmo

TALP Research CenterComputer Science Department

Universitat Politecnica de Catalunya

TA-Graph 2014

Coreference Resolution

FC Barcelona president Joan Laporta has warned Chelsea off

star striker Lionel Messi .

Aware of Chelsea owner Roman Abramovich ’s interest in the young Argentine ,

Laporta said last night: “ I will answer as always, Messi is not for sale and we

do not want to let him go.”

Laporta said last night: “ I will answer as always, Messi is not for sale and we

do not want to let him go.”

Laporta said last night: “ I will answer as always, Messi is not for sale and we do

not want to let him go.”

Architecture of coreference resolution systems

Preprocessing:I Mention detection:

I Detect the boundaries of the mentions in the input text.I m = (m1,m2, . . . ,mn) ordered as found in the document.

Preprocessing:I Characterization of mentions.

I Feature engineering: Part of speech, syntactic functions,semantic classes...

Resolution:I Classification.

I Evaluates the compatibility of elements in order to corefer.I Elements can be mentions or partial entities.I Commonly, binary classifier. Classes: CO (coreferential), NC

(not coreferential)I Confidence values or probabilities associated with the class.I Classifiers can also use rankers and constraints.

Resolution:I Linking.

I Links mentions and partial entities to form the final entities.I Input: the output of the classification process (classes and

probabilities).I Diversity of algorithms: clustering, graph partitioning,

heuristics, single-link, ILP...

Approaches

Mention pairs

I Binary classification of all possible mention pairs.I Drawbacks:

I Lack of information (e.g. uderspecified gender or number)I Transitivity contradictions (A⇔B, B⇔C, C 6⇔A)

Entity-mention

I Binary classification of each mention paired with partialentities built so far.

Rankers

I Classification is not binary: Candidates (either mentions orpartial entities) are ranked and the best one is chosen.

I Drawbacks:I Forced to pick one (or to use anaphoricity filter, or a

confidence threshold).

Our hypergraph partitioning approach

I Hypergraph partitioning: problem representation.I Avoids linking contradictions

I Constraints: symbolic knowledge

I Compatible with mention-pair and entity-mention modelsI Can be both automatically learned and manually written.I Easy incorporation of new information (world knowledge,

discourse coherence).

I Relaxation labeling: resolution algorithm

I Performs function optimization based on local information.I Iterative resolution.I Use mention-pair and entity-mention models.I More confidence links are solved first.I Avoids errors caused by lack of information or context.

I Constraints: symbolic knowledgeI Compatible with mention-pair and entity-mention modelsI Can be both automatically learned and manually written.I Easy incorporation of new information (world knowledge,

I Relaxation labeling: resolution algorithm

I Performs function optimization based on local information.I Iterative resolution.I Use mention-pair and entity-mention models.I More confidence links are solved first.I Avoids errors caused by lack of information or context.

I Constraints: symbolic knowledgeI Compatible with mention-pair and entity-mention modelsI Can be both automatically learned and manually written.I Easy incorporation of new information (world knowledge,

I Relaxation labeling: resolution algorithmI Performs function optimization based on local information.I Iterative resolution.I Use mention-pair and entity-mention models.I More confidence links are solved first.I Avoids errors caused by lack of information or context.

Problem representation

I Mentions ⇒ VerticesI Each mention mi is represented as a vertex vi ∈ V.

I Constraints ⇒ Edges

I A hyperedge eg ∈ E for every group (g) of mentions satisfyinga constraint.

I Constraint weights ⇒ Edge weights

I Positive weight: mentions should coreferI Negative weight: mentions should not coreferI The higher the (absolute) weight, the more reliable.

I Constraints ⇒ EdgesI A hyperedge eg ∈ E for every group (g) of mentions satisfying

a constraint.

I Constraint weights ⇒ Edge weights

I Positive weight: mentions should coreferI Negative weight: mentions should not coreferI The higher the (absolute) weight, the more reliable.

I Constraints ⇒ EdgesI A hyperedge eg ∈ E for every group (g) of mentions satisfying

a constraint.

I Constraint weights ⇒ Edge weightsI Positive weight: mentions should coreferI Negative weight: mentions should not coreferI The higher the (absolute) weight, the more reliable.

Examples of constraints

I Example of a pair constraint:DIST SEN 1(0,1) & GENDER YES(0,1) & SRL ARG 0(0) &SRL ARG 0(1) & TYPE P(0) & TYPE P(1)Example:He0 comes early. He1 leaves late.

I Example of a group constraint:DIST SEN 1(0,1) & DIST SEN 1(1,2) & AGREEMENT YES(0,1,2) &TYPE E(0) & TYPE S(1) & TYPE E(2)Example:Barak Obama0 will not adress the press today.The president1 is receiving a foreing delegation in the White House.Obama2’s popularity is at its lowest this week.

I Example of an entity-mention constraint:SRL ARG 0(0) & SRL ARG 1(1) & SRL ARG 0(2) & SRL ARG 1(3) &DIST SEN 0(0, 1) & DIST SEN 1(1, 2) & DIST SEN 0(2, 3) &AGREEMENT YES(0, 2) & AGREEMENT YES(1, 3)Influence rule: (0, 2)A, (1)B ⇒ (3)BExample:Charlie0 called Bob1.He2 invited him3 to the party.

Relaxation Labeling

Evaluation

CoNLL-2011 shared task:Modeling unrestricted coreference in OntoNotes

I RelaxCor (sapena) achieved 2nd position (out of 18participants) in the official competition.

Pos System MUC F1 B3 F1 CEAFe F1 (MUC+B3+CEAFe)/3

1 Lee 59.57 68.31 45.48 57.792 Sapena 59.55 67.09 41.32 55.993 Chang 57.15 68.79 41.94 55.964 Nugues 58.61 65.46 39.52 54.535 Santos 56.65 65.66 37.91 53.416 Song 59.95 63.23 35.96 53.057 Stoyanov 58.43 61.44 35.28 51.92... ... ... ... ... ...16 Kummerfeld 42.70 60.29 38.32 47.1017 Zhekova 24.08 61.46 35.75 40.4318 Irwin 19.98 50.46 25.21 31.28

What is this useful for?

This morning Obama presented a newlaw proposal. The president said that theproposal improves citizens life quality.

What is this useful for?

This morning Obama presented a newlaw proposal. The president said that theproposal improves citizens life quality.

A constraint-based hypergraph partitioningapproach to coreference resolution

Emili Sapena, Lluıs Padro, Jordi Turmo

TALP Research CenterComputer Science Department

Universitat Politecnica de Catalunya

TA-Graph 2014

A constraint-based hypergraph partitioning approach to ......I Linking. I Links mentions and partial...

Documents

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Hypergraph expanders of all uniformities from Cayley graphsdconlon/hypergraphexpanders2.pdf · struction, which can be randomized, of 3-uniform hypergraph expanders with polylogarithmic