A GRAPH-BASED CROSS-LINGUAL PROJECTION APPROACH FOR WEAKLY SUPERVISED RELATION EXTRACTION The 50 th Annual Meeting of the Association for Computational Linguistics (ACL 2012) July 11 th , 2012, Jeju Seokhwan Kim (Institute for Infocomm Research) Gary Geunbae Lee (POSTECH)
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A Graph-based Cross-lingual Projection Approach for Weakly Supervised Relation Extraction
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A GRAPH-BASED CROSS-LINGUAL
PROJECTION APPROACH FOR
WEAKLY SUPERVISED RELATION EXTRACTION
The 50th Annual Meeting of the Association for Computational Linguistics (ACL 2012)
July 11th, 2012, Jeju
Seokhwan Kim (Institute for Infocomm Research)
Gary Geunbae Lee (POSTECH)
Contents
• Introduction
• Methods
Cross-lingual Annotation Projection for Relation Extraction
Graph-based Projection Approach
• Evaluation
• Conclusions
2
Contents
• Introduction
• Methods
Cross-lingual Annotation Projection for Relation Extraction
Graph-based Projection Approach
• Evaluation
• Conclusions
3
Problem Definition
• Relation Extraction
To identify semantic relations between a pair of entities
Considered as a classification problem
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Honolulu was Barack Obama , in
Birthplace
Hawaii born . PER LOC LOC
Related Work (1)
• Supervised Learning
Many supervised machine learning approaches have been
successfully applied
• (Kambhatla, 2004; Zhou et al., 2005; Zelenko et al., 2003; Culotta and
Sorensen, 2004; Bunescu and Mooney, 2005; Zhang et al., 2006)
• Semi-supervised Learning
To obtain the annotations of unlabeled instances from the seed
information
• (Brin, 1999; Riloff and Jones, 1999; Agichtein and Gravano, 2000;
Sudo et al, 2003; Yangarber, 2003; Stevenson and Greenwood, 2006;
Zhang, 2004; Chen el al., 2006; Zhou et al., 2009)
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Motivation
• Resources for Relation Extraction
Supervised/Semi-supervised Approaches
• Labeled corpora for supervised learning
• Seed instances for semi-supervised learning
• Available for only a few languages
ACE 2003 Multilingual Training Dataset
• English (252 articles)
• Chinese (221 articles)
• Arabic (206 articles)
• No resources for other languages
Korean
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Related Work (2)
• Self-supervised Learning
To obtain the annotated dataset without any human effort
Using the information obtained from external resources
• Heuristic-based Method (Banko et al., 2007; Banko et al., 2008)
• Wikipedia-based Methods (Wu and Weld, 2010)
• Cross-lingual Annotation Projection
To leverage parallel corpora to project the relation annotations on
the resource-rich source language to the resource-poor target
language (Kim et al., 2010, Kim et al., 2011)
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Contents
• Introduction
• Methods
Cross-lingual Annotation Projection for Relation Extraction
Graph-based Projection Approach
• Implementation
• Evaluation
• Conclusions
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Overall Architecture
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Projection Annotation Parallel Corpus
Sentences in Ls
Preprocessing (POS Tagging,
Parsing)
NER
Relation Extraction
Annotated Sentences in
Ls
Sentences in Lt
Preprocessing (POS Tagging,
Parsing)
Word Alignment
Projection
Annotated Sentences in
Lt
Direct Projection
• Annotation
• Projection
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fE (<Barack Obama, Honolulu>) = 1
Barack Obama was born in Honolulu Hawaii , .
버락 오바마 (beo-rak-o-ba-ma)
는 (neun)
하와이 (ha-wa-i)
호놀룰루 (ho-nol-rul-ru)
의 (ui)
에서 (e-seo)
태어났다 (tae-eo-nat-da)
fK (<버락 오바마, 호놀룰루>) = 1
(Kim et al., 2010)
Limitations of Direct Projection
• Direct projection approach is still vulnerable to the
erroneous inputs generated by preprocessors
• Main causes of this limitation
Considering alignment between entity candidates only, not any
contextual information
Performed by just a single pass process
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Graph-based Learning
• Semi-supervised learning algorithm
• Defining a graph
The nodes represent labeled and unlabeled examples in a dataset
The edges reflect the similarity of examples
• Learning a labeling function in an iterative manner
It should be close to the given labels on the similar labeled nodes
It should be smooth on the whole graph
• Related Work
Graph-based Learning for Relation Extraction (Chen et al, 2006)
Bilingual projection of POS tagging (Das and Petrov, 2011)
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Graph Construction
• Graph Nodes
Instance Nodes
• Defined for all pairs of entity candidates in both languages
• Each instance node has a soft label vector Y = [y+ y-]
Context Nodes
• For identifying the relation descriptors of the positive instances
• Defined for each trigram which is located between a given entity pair
which is semantically related
• Each context node has a soft label vector Y = [y+ y-]
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<ARG1> was born in <ARG2>
<ARG1> was born was born in born in <ARG2>
Graph Construction
• Edge Weights
Between instance node and context node in the same language
Between context nodes in a language
Between context nodes in source and target languages