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Jointly Identifying Temporal Relations with Markov Logic. Katsumasa Yoshikawa † , Sebastian Riedel ‡ , Masayuki Asahara † , Yuji Matsumoto † † Nara Institute of Science and Technology, Japan ‡ University of Massachusetts, Amherst. ACL-IJCNLP 2-7 August, 2009 Suntec Singapore. Outline.

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jointly identifying temporal relations with markov logic

Jointly Identifying Temporal Relations with Markov Logic

Katsumasa Yoshikawa†, Sebastian Riedel‡, Masayuki Asahara†, Yuji Matsumoto†

†Nara Institute of Science and Technology, Japan

‡ University of Massachusetts, Amherst

ACL-IJCNLP2-7 August, 2009 Suntec Singapore

outline
Outline

Background and Motivation

Related work of temporal relation identification

Proposed global approach with Markov Logic

Experimental setup and highlighted data

Summary and future work

background and motivation
Background and Motivation

Temporal Relation Identification (temporal ordering)

Identifying temporal orders of events and time expressions in a document

With the introduction of the TimeBank corpus (Pustejovsky et al., 2003), machine learning approaches to temporal ordering became possible.

Document Creation Time(August 2009)

2003

introduction

became

Past

Present

Future

BEFORE

Essential work for document understanding

outline1
Outline

Background and Motivation

Related work of temporal relation identification

Proposed global approach with Markov Logic

Experimental setup and highlighted data

Summary and future work

allen s temporal logic allen 1983 timeml and timebank pustejovsky et al 2003
We regard temporal ordering as a classification task

With TimeML, the TimeBank corpus was created

Allen‘s Temporal Logic [Allen 1983]TimeML and TimeBank [Pustejovsky et al. 2003]

Allen’s

(13 Labels)‏

TimeML

(11 Labels)‏

EVENT / TIME

before

<

BEFORE

meets

m

IBEFORE

overlaps

o

ENDED_BY

finished-by

fi

INCLUDES

contains

c

starts

s

BEGINS

equal

=

SIMULTANEOUS

started-by

si

BEGUN_BY

during

d

DURING

finishes

f

ENDS

overlapped-by

oi

met-by

mi

IAFTER

after

AFTER

>

tempeval semeval 2007 task 15
TempEval (SemEval 2007 Task 15)

Temporal Relation Identification in SemEval 2007 Shared Task (TempEval)

Six temporal relation labels

Main Label (BEFORE, AFTER,OVERLAP)

Sub-Label (BEFORE-OR-OVERLAP, OVERLAP-OR-AFTER, VAGUE)

TempEval includes three types of tasks (A, B, and C)

slide7

Task A of TempEval

  • Temporal relations between events and time expressions that occur within the same sentence

With the introduction of the TimeBank corpus (Pustejovsky et al., 2003), machine learning approaches to temporal ordering became possible.

2003

DCT

(August 2009)

OVERLAP

introduction

became

Past

Present

Future

slide8

Task B of TempEval

  • Temporal relations between events and the Document Creation Time (DCT)

With the introduction of the TimeBank corpus (Pustejovsky et al., 2003), machine learning approaches to temporal ordering became possible.

2003

DCT

(August 2009)

BEFORE

BEFORE

introduction

became

Past

Present

Future

slide9

Task C of TempEval

  • Temporal relations between the main events of adjacent sentences

The TimeBank corpus was created (Pustejovsky et al., 2003). As a result, machine learning approaches to temporal ordering became possible.

2003

DCT

(August 2009)

created

became

BEFORE

Past

Present

Future

issues of the tempeval participants
Issues of the TempEval Participants

Local approaches with machine learning are employed by many participants in TempEval

Considering only a single relation at a time

Local approach cannot take into account the other relations

DCT‏

DCT‏

BEFORE(Task B)

AFTER(Task B)

EVENT 2‏

EVENT 1

EVENT 1

EVENT 2‏

AFTER ?(Task C)

BEFORE(Task C)

A globalapproach can be useful in that case

issues of the tempeval participants1
Issues of the TempEval Participants

Local approaches with machine learning are employed by many participants in TempEval

Considering only a single relation at a time

Local approach cannot take into account the other relations

DCT‏

BEFORE(Task B)

AFTER(Task B)

EVENT 1

EVENT 2‏

BEFORE(Task C)

A globalapproach can be useful in that case

outline2
Outline

Background and Motivation

Related work and task reviews of temporal relation identification

Proposed global approach with Markov Logic

Experimental setup and highlighted data

Summary and future work

overview of our global approach
Overview of Our Global Approach

Ensure consistency among the multiple relations with hard and soft constraints based on the transition rules

Jointly identify the three types of relations in TempEval

Learning one global model for the three tasks

Global approach withMarkov Logic

markov logic richardson and domingos 2006
Markov Logic[Richardson and Domingos, 2006]

A Statistical Relational Learning framework

An expressive template language of Markov Networks

Not only hard but alsosoft constraints

A Markov Logic Network (MLN) is a set of pairs (φ, w) where

φ is a formula in first-order logic

w is a real number weight

Higher weight  stronger constraint

an example of markov logic networks
An Example of Markov Logic Networks

hasPastTense(a) : indicates that an event a has past tense

beforeDCT(a) : indicates that an event a happens before the DCT

before(a,b) : indicates that an event a happens before another event b

hasPastTense(e1)

before (e1,e2)

hasPastTense(e2)

wa(e1)

wa(e2)

wb(e1,e2)

grounding

beforeDCT(e1)

beforeDCT(e2)

※ e1 and e2 are events

global feature representation predicate definition
Global Feature Representation (Predicate Definition)
  • relE2T(e, t, r) : the relation r between an event e and a time expression t
  • relDCT(e, r) : the relation r between an event a and the DCT
  • relE2E(e1, e2, r) : the relation r between two events e1 and e2
  • relT2T(t1, t2, r) : the relation r between two time expressions t1 and t2
  • dctOrder(t, r) : the relation r between a time expression t and the DCT

DCT

dctOrder

dctOrder

relT2T

TIME (t1)

TIME (t2)

relDCT(B)‏

relDCT(B)‏

relE2T(A)‏

relE2T(A)‏

EVENT (e1)

EVENT (e2)

relE2E(C)‏

global feature representation transition rules
Global Feature Representation (Transition Rules)
  • We jointly solve the three tasks of TempEval
  • We use global features named Joint formulae
  • A joint formula is based on a transition rule

DCT

B→C

DCT

C→B

BEFORE

BEFORE

AFTER

BEFORE

EVENT (e1)‏

EVENT(e2)‏

EVENT (e2)‏

EVENT(e1)‏

BEFORE

AFTER

BEFORE & AFTER ⇒ BEFORE

BEFORE & AFTER ⇒ BEFORE

If e1 happens before DCT and e2 happens after DCT => then e1 is before e2

If e1 happens before DCT and e1 happens after e2, => then e2 happens before DCT

global feature representation templates of the all joint formulae
Global Feature Representation (Templates of the all Joint Formulae)

They are developed with events, time expressions and relations

global feature representation templates of the all joint formulae1
Global Feature Representation (Templates of the all Joint Formulae)

They are developed with events, time expressions and relations

outline3
Outline

Background and Motivation

Related work and task reviews of temporal relation identification

Proposed global approach with Markov Logic

Experimental setup and highlighted data

Summary and future work

experimental setup
Experimental Setup

Use a MLN Engine “Markov thebeast”

Weight learning : MIRA

Inference : Cutting Plane Inference (base solver: ILP) [Riedel, 2008]

Employ the local features referred to the early work in TempEval [SemEval, 2007]

Select joint formulae as global features

Use the same data and evaluation schemes of TempEval

comparison of local and global
Comparison of Local and Global

Over all tasks, Global is better than Local

On Task A, Global model outperformed Local one.

  • Results with 10-fold cross validation on training data

※All scores denote F1-value

ρ< 0.01 (McNemar’s test, 2-tailed)

comparison to state of the art
Comparison to State-of-the-art

Outperformed the others on Tasks A and C

Always performed better than the best pure machine-learning based system (CU-TMP[Bethard and Martin, 2007])

  • Results with the other systems on test data (F1-value)

※All scores denote F1-value

outline4
Outline

Background and motivation

Related work and task reviews of temporal relation identification

Proposed global approach with Markov Logic

Experimental setup and highlighted data

Summary and future work

summary
Summary

We proposed a global framework with Markov Logic for Temporal Relation Identification

Our global model with joint formulae successfully improved the performances of the identifications

Our approach reported the competitive results among all participants in TempEval

future work
Future Work

Issues inherent to the task and the dataset

Low inter annotator agreement

Low transitive connectivity

Small size

  • Numbers of labeled relations for all tasks and datasets
  • Semi-supervised approaches ease some issues