1. A CROSS-LINGUAL ANNOTATION PROJECTION
APPROACH FOR RELATION DETECTION
The 23rd International Conference on Computational Linguistics (COLING 2010)
August 24th, 2010, Beijing
Seokhwan Kim (POSTECH)
Minwoo Jeong (Saarland University)
Jonghoon Lee (POSTECH)
Gary Geunbae Lee (POSTECH)

2. Contents
• Introduction
• Methods
 Cross-lingual Annotation Projection for Relation Detection
 Noise Reduction Strategies
• Evaluation
• Conclusion
2

3. Contents
• Introduction
• Methods
 Cross-lingual Annotation Projection for Relation Detection
 Noise Reduction Strategies
• Evaluation
• Conclusion
3

4. What’s Relation Detection?
• Relation Extraction
 To identify semantic relations between a pair of entities
 ACE RDC
• Relation Detection (RD)
• Relation Categorization (RC)
Owner-Of
Jan Mullins, owner of Computer Recycler Incorporated said that …
4

5. What’s the Problem?
• Many supervised machine learning approaches have been
successfully applied to the RDC task
 (Kambhatla, 2004; Zhou et al., 2005; Zelenko et al., 2003; Culotta
and Sorensen, 2004; Bunescu and Mooney, 2005; Zhang et al.,
2006)
• Datasets for relation detection
 Labeled corpora for supervised learning
 Available for only a few languages
• English, Chinese, Arabic
 No resources for other languages
• Korean
5

6. Contents
• Introduction
• Methods
 Cross-lingual Annotation Projection for Relation Detection
 Noise Reduction Strategies
• Evaluation
• Conclusion
6

7. Cross-lingual Annotation Projection
• Goal
 To learn the relation detector without significant annotation efforts
• Method
 To leverage parallel corpora to project the relation annotation on
the source language LS to the target language LT
7

8. Cross-lingual Annotation Projection
• Previous Work
 Part-of-speech tagging (Yarowsky and Ngai, 2001)
 Named-entity tagging (Yarowsky et al., 2001)
 Verb classification (Merlo et al., 2002)
 Dependency parsing (Hwa et al., 2005)
 Mention detection (Zitouni and Florian, 2008)
 Semantic role labeling (Pado and Lapata, 2009)
• To the best of our knowledge, no work has reported on the
RDC task
8

9. Overall Architecture
Annotation Parallel
Projection
Corpus
Sentences in Sentences in
Ls Lt
Preprocessing Preprocessing
(POS Tagging, (POS Tagging,
Parsing) Parsing)
NER Word Alignment
Relation Detection Projection
Annotated Annotated
Sentences in Sentences in
Ls Lt 9

10. How to Reduce Noise?
• Error Accumulation
 Numerous errors can be generated and accumulated through a
procedure of annotation projection
• Preprocessing for LS and LT
• NER for LS
• Relation Detection for LS
• Word Alignment between LS and LT
• Noise Reduction
 A key factor to improve the performance of annotation projection
10

11. How to Reduce Noise?
• Noise Reduction Strategies (1)
 Alignment Filtering
• Based on Heuristics
 A projection for an entity mention should be based on alignments between
contiguous word sequences
accepted rejected
11

12. How to Reduce Noise?
• Noise Reduction Strategies (1)
 Alignment Filtering
• Based on Heuristics
 A projection for an entity mention should be based on alignments between
contiguous word sequences
 Both an entity mention in LS and its projection in LT should include at
least one base noun phrase
N N N N
accepted rejected accepted rejected
N
12

13. How to Reduce Noise?
• Noise Reduction Strategies (1)
 Alignment Filtering
• Based on Heuristics
 A projection for an entity mention should be based on alignments between
contiguous word sequences
 Both an entity mention in LS and its projection in LT should include at
least one base noun phrase
 The projected instance in LT should satisfy the clausal agreement with the
original instance in LS
N N N N
accepted rejected accepted rejected rejected
N
13

14. How to Reduce Noise?
• Noise Reduction Strategies (2)
 Alignment Correction
• Based on a bilingual dictionary for entity mentions
 Each entry of the dictionary is a pair of entity mention in LS and its
translation or transliteration in LT
FOR each entity ES in LS A B C D E F G
RETRIEVE counterpart ET from DICT(E-T)
SEEK ET from the sentence ST in LT
IF matched THEN BCD - βγ
MAKE new alignment ES-ET
ENDIF
ENDFOR α β γ δ ε δ ε
corrected
14

15. How to Reduce Noise?
• Noise Reduction Strategies (3)
 Assessment-based Instance Selection
• Based on the reliability of a projected instances in LT
 Evaluated by the confidence score of monolingual relation detection for
the original counterpart instance in LS
 Only instances with larger scores than threshold value θ are accepted
conf = 0.9 conf = 0.6
θ = 0.7
accepted rejected
15

16. Contents
• Introduction
• Methods
 Cross-lingual Annotation Projection for Relation Detection
 Noise Reduction Strategies
• Evaluation
• Conclusion
16

17. Experimental Setup
• Dataset
 English-Korean parallel corpus
• 454,315 bi-sentence pairs in English and Korean
• Aligned by GIZA++
 Korean RDC corpus
• Annotated following LDC guideline for ACE RDC corpus
• 100 news documents in Korean
 835 sentences
 3,331 entity mentions
 8,354 relation instances
17

18. Experimental Setup
• Preprocessors
 English
• Stanford Parser (Klein and Manning, 2003)
• Stanford Named Entity Recognizer (Finkel et al., 2005)
 Korean
• Korean POS Tagger (Lee et al., 2002)
• MST Parser (R. McDonald et al., 2006)
18

19. Experimental Setup
• Relation Detection for English Sentences
 Tree kernel-based SVM classifier
• Training Dataset
 ACE 2003 corpus
• 674 documents
• 9,683 relation instances
• Model
 Shortest path enclosed subtrees kernel (Zhang et al., 2006)
• Implementation
 SVM-Light (Joachims, 1998)
 Tree Kernel Tools (Moschitti, 2006)
19

20. Experimental Setup
• Relation Detection for Korean Sentences
 Tree kernel-based SVM classifier
• Training Dataset
 Half of the Korean RDC corpus (baseline)
 Projected instances
• Model
 Shortest path dependency kernel (Bunescu and Mooney, 2005)
• Implementation
 SVM-Light (Joachims, 1998)
 Tree Kernel Tools (Moschitti, 2006)
20

21. Experimental Setup
• Experimental Sets
 Combinations of noise reduction strategies
• (S1: Heuristic, S2: Dictionary, S3: Assessment)
1. Baseline
 Trained with only half of the Korean RDC corpus
2. Baseline + Projections (no noise reduction)
3. Baseline + Projections (S1)
4. Baseline + Projections (S1 + S2)
5. Baseline + Projections (S3)
6. Baseline + Projections (S1 + S3)
7. Baseline + Projections (S1 + S2 + S3)
21

22. Experimental Setup
• Evaluation
 On the second half of the Korean RDC corpus
• The first half is for the baseline
 On true entity mentions with true chaining of coreference
 Evaluated by Precision/Recall/F-measure
22

23. Experimental Results
no assessment with assessment
Model
P R F P R F
baseline 60.5 20.4 30.5 - - -
baseline + projection 22.5 6.5 10.0 29.1 13.2 18.2
Baseline + projection
51.4 15.5 23.8 56.1 22.9 32.5
(heuristics)
Baseline + projection
55.3 19.4 28.7 59.8 26.7 36.9
(heuristics + dictionary)
23

24. Non-filtered Projects were Poor
no assessment with assessment
Model
P R F P R F
baseline 60.5 20.4 30.5 - - -
baseline + projection 22.5 6.5 10.0 29.1 13.2 18.2
Baseline + projection
51.4 15.5 23.8 56.1 22.9 32.5
(heuristics)
Baseline + projection
55.3 19.4 28.7 59.8 26.7 36.9
(heuristics + dictionary)
24

25. Heuristics Were Helpful
no assessment with assessment
Model
P R F P R F
baseline 60.5 20.4 30.5 - - -
baseline + projection 22.5 6.5 10.0 29.1 13.2 18.2
Baseline + projection
51.4 15.5 23.8 56.1 22.9 32.5
(heuristics)
Baseline + projection
55.3 19.4 28.7 59.8 26.7 36.9
(heuristics + dictionary)
25

26. Much Worse Than Baseline
no assessment with assessment
Model
P R F P R F
baseline 60.5 20.4 30.5 - - -
baseline + projection 22.5 6.5 10.0 29.1 13.2 18.2
Baseline + projection
51.4 15.5 23.8 56.1 22.9 32.5
(heuristics)
Baseline + projection
55.3 19.4 28.7 59.8 26.7 36.9
(heuristics + dictionary)
26

27. Dictionary Was Also Helpful
no assessment with assessment
Model
P R F P R F
baseline 60.5 20.4 30.5 - - -
baseline + projection 22.5 6.5 10.0 29.1 13.2 18.2
Baseline + projection
51.4 15.5 23.8 56.1 22.9 32.5
(heuristics)
Baseline + projection
55.3 19.4 28.7 59.8 26.7 36.9
(heuristics + dictionary)
27

28. Still Worse Than Baseline
no assessment with assessment
Model
P R F P R F
baseline 60.5 20.4 30.5 - - -
baseline + projection 22.5 6.5 10.0 29.1 13.2 18.2
Baseline + projection
51.4 15.5 23.8 56.1 22.9 32.5
(heuristics)
Baseline + projection
55.3 19.4 28.7 59.8 26.7 36.9
(heuristics + dictionary)
28

29. Assessment Boosted Performance
no assessment with assessment
Model
P R F P R F
baseline 60.5 20.4 30.5 - - -
baseline + projection 22.5 6.5 10.0 29.1 13.2 18.2
Baseline + projection
51.4 15.5 23.8 56.1 22.9 32.5
(heuristics)
Baseline + projection
55.3 19.4 28.7 59.8 26.7 36.9
(heuristics + dictionary)
29

30. Combined Strategies Achieved
Better Performance Then Baseline
no assessment with assessment
Model
P R F P R F
baseline 60.5 20.4 30.5 - - -
baseline + projection 22.5 6.5 10.0 29.1 13.2 18.2
Baseline + projection
51.4 15.5 23.8 56.1 22.9 32.5
(heuristics)
Baseline + projection
55.3 19.4 28.7 59.8 26.7 36.9
(heuristics + dictionary)
30

31. Contents
• Introduction
• Methods
 Cross-lingual Annotation Projection for Relation Detection
 Noise Reduction Strategies
• Evaluation
• Conclusion
31

32. Conclusion
• Summary
 A cross-lingual annotation projection for relation detection
 Three strategies for noise reduction
 Projected instances from an English-Korean parallel corpus helped
to improve the performance of the task
• with the noise reduction strategies
• Future work
 A cross-lingual annotation projection for relation categorization
 More elaborate strategies for noise reduction to improve the
projection performance for relation extraction
32

33. Q&A