Easy-First Coreference Resolution Veselin Stoyanov and Jason Eisner Johns Hopkins University

Easy-First Coreference Resolution

Veselin Stoyanov and Jason EisnerJohns Hopkins University

Coreference Resolution

• Given a peace of text, identify all noun phrases that refer to the same real-world entity



Former Boxing Champion Mike Tyson revealed that he had caught Hollywood heartthrob Brad Pitt with his ex-wife Robin Givens.In an interview Tyson said that he was amidst the process of divorce from Givens in the late 1980s when he found her sleeping with the Hollywood star.








• Typical pairwise approaches• But, inherently global task



• Inherently global task



• Inherently global task


Global Approaches to Coreference Resolution

• A difficult search problem– Exponentially many possible clusterings

• Recent approaches have been successful:– [Culotta et al., 2007] – train a global coherence

measure; use greedy inference at test time– [Poon and Domingos, 2008] – use an unsupervised

approach based on MLNs to enforce global properties

A Different Approach

• A Multi-Pass Sieve [Raghunathan et al., 2010]– Build coreference clusters incrementally– Applying sieves (hand-specified rules) of

decreasing precision and increasing recall– Winner of the CoNLL-2011 shared task

A Multi-Pass Sieve for Coreference Resolution [Raghunathan et al. 2010]



• Sieves:1. Exact match



• Sieves:1. Exact match2. Precise Constructs



• Sieves:1. Exact match2. Precise Constructs



• Sieves:1. Exact match2. Precise Constructs3. Strict Head match



• Sieves:1. Exact match2. Precise Constructs3. Strict Head match



• Sieves:1. Exact match2. Precise Constructs3. Strict Head match………….

7. Pronoun MatchingFormer Boxing Champion Mike Tyson revealed that he had caught Hollywood heartthrob Brad Pitt with his ex-wife Robin Givens.In an interview Tyson said that he was amidst the process of divorce from Givens in the late 1980s when he found her sleeping with the Hollywood star.


• Sieves:1. Exact match2. Precise Constructs3. Strict Head match………….

7. Pronoun MatchingFormer Boxing Champion Mike Tyson revealed that he had caught Hollywood heartthrob Brad Pitt with his ex-wife Robin Givens.In an interview Tyson said that he was amidst the process of divorce from Givens in the late 1980s when he found her sleeping with the Hollywood star.


• Use the same idea:– Greedily build clusters– Using more information for the less confident

decisions• But learn automatically what constitutes an

“easy” (confident) decision– Leads to empirical improvement over the Multi-

Pass Sieve approach and a competitive baseline


• Incrementally and greedily build clusters– Using machine learning

• Learn policy for the test time inference procedure

Easy-First Coreference: Talk Overview

• Test-Time Inference• Training Algorithm• Experimental results

TEST-TIME INFERENCE

Test-Time Inference

• Begin with each NP in a separate cluster


Test-Time Inference


Cluster 1SemType: uknwnGender: uknwnNumber: singleNames: NIL

Cluster 2SemType: personGender: maleNumber: singleNames: Mike Tyson

Cluster 3SemType: personGender: maleNumber: singleNames: NIL

Cluster 3SemType: personGender: maleNumber: singleNames: NIL

• Begin with each NP in a separate cluster

• Compute a feature vector for each pair of clusters

Test-Time Inference





Test-Time Inference




SemTypeSame = False


Test-Time Inference




SemTypeSame = FalseSemTypeComp = True


Test-Time Inference




SemTypeSame = FalseSemTypeComp = TrueNumberSame = True


Test-Time Inference




SemTypeSame = FalseSemTypeComp = TrueNumberSame = TrueRoleAppositive = True


Test-Time Inference




SemTypeSame = FalseSemTypeComp = TrueNumberSame = TrueRoleAppositive = TrueSentDistance = 0


Test-Time Inference




SemTypeSame = FalseSemTypeComp = TrueNumberSame = TrueRoleAppositive = TrueSentDistance = 0

Globalfeatures

Localfeatures


Test-Time Inference




f(joinij)

• Compute a weight for each join action

Test-Time Inference




w.f(joinij)

• Compute a weight for each join action

Test-Time Inference




w.f(joinij)weightij=

• Perform the action with the highest weight

Test-Time Inference




w.f(joinij)aij=argmaxij


Test-Time Inference




w.f(joinij)a12=argmaxij


Test-Time Inference



w.f(joinij)a12=argmaxij


Test-Time Inference



Test-Time Inference



Test-Time Inference



Test-Time Inference



Test-Time Inference



Test-Time Inference



Test-Time Inference



Test-Time Inference



Test-Time Inference



Test-Time Inference



Test-Time Inference


• Until the action with the highest weight is the special action HALT

Test-Time Inference


Time complexity

• Let n = the number of mentions in a document• At most n clusters at any time• s = number of joins performed by the

algorithm• s<=n at most n joins before HALT (usually

much less)

Time complexity

• Each step requires:– Popping the top action -- O(logn) (priority queue)– Performing the join – O(1)– Deleting O(n) old actions – O(nlogn)– Computing scores for O(n) new actions -- O(n)– Inserting those -- O(nlogn) or O(n)

• Total runtime = O(n2logn)+O(snlogn)

TRAINING ALGORITHM

Learning

• Learn weight vector w that leads the Easy-First algorithm to good clusterings

• At each step we are given a state s and a set of possible actions {joinij}∪{HALT}

• We want to learn what is “the best” action in each state

• But the current state s depends on the action taken so far

Reinforcement Learning

• A classic reinforcement learning setting• Act in the world and learn “good” actions in

each state• We tried several reinforcement learning

algorithms– Policy gradient [Sutton et al., 2000]– Q-learning [Watkins and Dayan, 1992]– No success: distant supervision; too many possible

states

Perceptron-Style Training

• A version of the early update structured perceptron [Collins and Roark, 2004]– Inspired by the success of easy-first parsing

[Goldberg and Elhadad, 2010]• The algorithm:

– Keep acting in the world until the next action will result in an error (reduction in score)

– Update weights away from the top action and towards the highest scoring positive action

Training algorithm

• Perform N iterations:– For each document d in the training set do:

• Initialize C so that each mention in its own cluster• Initialize A = {joinij}∪{HALT}

• repeat until amax != HALT:– for k steps do:

» amax =A.peekMax()

» if amax.isPositive() break

» apos =A.peekMaxPositive()

» w+=(feat(apos,C)-feat(amax ,C))» Recompute scores in A using new w

update weight

Training algorithm

• Perform N iterations:– For each document d in the training set do:

• Initialize C so that each mention in its own cluster• Initialize A = {joinij}∩{HALT}

• repeat until amax != HALT:– Update weights– amax =A.popMax()

– if amax is a join

» perform amax

EXPERIMENTAL EVALUATION

Experimental Setup

• Five datasets:– MUC6, MUC7, ACE03, ACE04, ACE05

• Two evaluation metrics: – MUC score– B^3 score

• Implemented in the Reconcile research platform [Stoyanov et al. 2010]– Code available upon request

• Two settings:– Automatic mentions– Gold (manual) mentions

Baselines

• A pairwise baseline [Stoyanov et al. 2009]• Reimplementation of the pairwise baseline using

different features• Easy-First coreference resolution

– Easy-Firstpercep – weights from the pairwise baseline

– Easy-Firststruct – weights from our training algorithm

• Only for the gold mentions data– Entity-centered approach [Haghighi and Klein, 2009]– Multi-pass sieve [Raghunathan et al., 2010]

Results: Automatic Mentions

MUC6 MUC7 ACE03 ACE04 ACE05 B^3 MUC B^3 MUCB^3 MUC B^3 MUC B^3 MUC Reconcile 70.9 68.5 65.9 62.8 79.4 67.9 76.5 62.0 73.7 67.4


MUC6 MUC7 ACE03 ACE04 ACE05 B^3 MUC B^3 MUCB^3 MUC B^3 MUC B^3 MUC Reconcile 70.9 68.5 65.9 62.8 79.4 67.9 76.5 62.0 73.7 67.4Pairwise baseline 70.7 67.9 62.2 57.9 81.7 73.5 79.4 68.0 75.7 70.2






MUC6 MUC7 ACE03 ACE04 ACE05 B^3 MUC B^3 MUCB^3 MUC B^3 MUC B^3 MUC Reconcile 70.9 68.5 65.9 62.8 79.4 67.9 76.5 62.0 73.7 67.4Pairwise baseline 70.7 67.9 62.2 57.9 81.7 73.5 79.4 68.0 75.7 70.2Easy-First percep 71.5 68.4 64.4 58.1 82.8 74.1 80.3 68.4 76.0 70.0


MUC6 MUC7 ACE03 ACE04 ACE05 B^3 MUC B^3 MUCB^3 MUC B^3 MUC B^3 MUC Reconcile 70.9 68.5 65.9 62.8 79.4 67.9 76.5 62.0 73.7 67.4Pairwise baseline 70.7 67.9 62.2 57.9 81.7 73.5 79.4 68.0 75.7 70.2Easy-First percep 71.5 68.4 64.4 58.1 82.8 74.1 80.3 68.4 76.0 70.0Easy-First struct 72.1 68.2 64.6 57.7 83.1 74.5 80.9 68.8 75.9 70.9





Results: Gold Mentions

MUC6 ACE04

B^3 MUC B^3 MUC

Haghighi and Klein 75.0 81.9 76.9 76.5

Raghunathan et al. 73.2 77.7 78.9 78.1


MUC6 ACE04

B^3 MUC B^3 MUC



Stoyanov et al. 76.1 88.2 77.8 76.2


MUC6 ACE04

B^3 MUC B^3 MUC



Stoyanov et al. 76.1 88.2 77.8 76.2


MUC6 ACE04

B^3 MUC B^3 MUC



Stoyanov et al. 76.1 88.2 77.8 76.2


MUC6 ACE04

B^3 MUC B^3 MUC



Stoyanov et al. 76.1 88.2 77.8 76.2

Pairwise baseline 72.7 88.2 75.9 78.9


MUC6 ACE04

B^3 MUC B^3 MUC



Stoyanov et al. 76.1 88.2 77.8 76.2

Pairwise baseline 72.7 88.2 75.9 78.9


MUC6 ACE04

B^3 MUC B^3 MUC



Stoyanov et al. 76.1 88.2 77.8 76.2

Pairwise baseline 72.7 88.2 75.9 78.9Easy-First percep 73.3 88.2 78.4 79.8


MUC6 ACE04

B^3 MUC B^3 MUC



Stoyanov et al. 76.1 88.2 77.8 76.2

Pairwise baseline 72.7 88.2 75.9 78.9Easy-First percep 73.3 88.2 78.4 79.8Easy-First struct 77.5 88.2 81.8 80.1

Conclusions

• A new approach to coreference using greedy incremental clustering– More accurate clustering by utilizing global

information as it becomes available for less confident decisions

• Learning policy for the test-time inference procedure leads to improvements

THANK YOU!

Documents

Easy-First Coreference Resolution Veselin Stoyanov and Jason Eisner Johns Hopkins University