A Graph-based Cross-lingual Projection Approach for Weakly Supervised Relation Extraction
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This document describes a graph-based approach for cross-lingual projection of relation annotations from English to Korean. The approach constructs a graph with nodes for entity pairs and context words, connected by edges representing similarity. Label propagation is used to transfer annotations across the graph. Evaluation on four relations shows the graph-based approach improves over direct projection and other self-supervised methods, achieving a top F-measure of 76.3%. The approach helps alleviate errors from direct projection while leveraging contextual information.
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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-]
<ARG1> was born in <ARG2>
<ARG1> was born was born in born in <ARG2> 13](https://image.slidesharecdn.com/acl2012kim-120712222335-phpapp01/85/A-Graph-based-Cross-lingual-Projection-Approach-for-Weakly-Supervised-Relation-Extraction-13-320.jpg)
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mlss
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Exploring Convolutional and Recurrent Neural Networks in Sequential Labelling...
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The Fourth Dialog State Tracking Challenge (DSTC4)
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Wikification of Concept Mentions within Spoken Dialogues Using Domain Constra...
Wikification of Concept Mentions within Spoken Dialogues Using Domain Constra...Seokhwan Kim This paper presents an approach to link concept mentions in spoken dialogue transcripts to relevant concepts in Wikipedia, consisting of three steps: (1) analyzing properties of each mention, (2) generating candidate concepts based on the analysis, and (3) ranking candidates using a learning model. The approach is evaluated on tourist guide dialogues and shows improved performance over baselines that do not apply constraints from mention analysis or Wikipedia.
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Towards Improving Dialogue Topic Tracking Performances with Wikification of C...Seokhwan Kim This document proposes improving dialogue topic tracking by linking concept mentions in spoken dialogues to relevant Wikipedia entries through a process called wikification. It presents a method for wikifying mentions by ranking candidate Wikipedia concepts and adding wikification-based features to existing topic tracking models. An experiment on tourism dialogues found the additional wikification features improved accuracy, achieving up to 79.12% for topic prediction and 50.10% for transition detection.
A Composite Kernel Approach for Dialog Topic Tracking with Structured Domain ...
A Composite Kernel Approach for Dialog Topic Tracking with Structured Domain ...Seokhwan Kim This document discusses a composite kernel approach for dialog topic tracking using structured domain knowledge from Wikipedia. It presents a classification problem addressing topic transitions within dialogues, outlines the method of leveraging Wikipedia to enhance topic tracking performance, and provides experimental results showcasing the effectiveness of the proposed model compared to traditional approaches. The experiments involved mixed-initiative dialogs and demonstrated notable improvements in topic transition accuracy using the composite kernel methods.
Sequential Labeling for Tracking Dynamic Dialog States
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2) The method presented uses conditional random fields (CRFs) with a BIO tagging scheme and sequential labeling to predict dialog states. Features include SLU hypotheses and system actions.
3) Experimental results on the DSTC 2 dataset show the CRF models outperform maximum entropy baselines on goals and method tracking in accuracy and error, and achieve comparable results to the baseline on requested slots tracking.
Wikipedia-based Kernels for Dialogue Topic Tracking
Wikipedia-based Kernels for Dialogue Topic TrackingSeokhwan Kim This document presents a Wikipedia-based kernel method for dialogue topic tracking. It aims to incorporate various types of knowledge from Wikipedia to improve topic tracking, without requiring significant effort to build domain-specific resources. An evaluation on a dialogue corpus shows the proposed approach outperforms baselines in segmenting dialogues into coherent topic segments and identifying topic transitions, especially for system-initiative cases. Ongoing work involves using additional Wikipedia knowledge and presenting results at ACL 2014.
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A Graph-based Cross-lingual Projection Approach for Spoken Language Understan...Seokhwan Kim This paper proposes a graph-based projection approach to improve the robustness of cross-lingual spoken language understanding (SLU) when applied to a new language. The approach constructs graphs using trigrams in the dataset connected by word alignments and semantic similarities. It then performs label propagation to induce labels for unlabeled nodes. An evaluation on English-Korean SLU tasks shows the graph-based projection approach improves over direct projection and training only on the source language data, achieving higher precision, recall and accuracy for named entity recognition and dialog act identification.
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An alignment-based approach to semi-supervised relation extraction including ...
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- 1. 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)
- 2. Contents • Introduction • Methods Cross-lingual Annotation Projection for Relation Extraction Graph-based Projection Approach • Evaluation • Conclusions 2
- 3. Contents • Introduction • Methods Cross-lingual Annotation Projection for Relation Extraction Graph-based Projection Approach • Evaluation • Conclusions 3
- 4. Problem Definition • Relation Extraction To identify semantic relations between a pair of entities Birthplace Barack Obama was born in Honolulu , Hawaii . PER LOC LOC Considered as a classification problem 4
- 5. 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) 5
- 6. 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 6
- 7. 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) 7
- 8. Contents • Introduction • Methods Cross-lingual Annotation Projection for Relation Extraction Graph-based Projection Approach • Implementation • Evaluation • Conclusions 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 Projection Extraction Annotated Annotated Sentences in Sentences in Ls Lt 9
- 10. Direct Projection (Kim et al., 2010) • Annotation • Projection fE (<Barack Obama, Honolulu>) = 1 Barack Obama was born in Honolulu , Hawaii . 버락 오바마 는 하와이 의 호놀룰루 에서 태어났다 (beo-rak-o-ba-ma) (neun) (ha-wa-i) (ui) (ho-nol-rul-ru) (e-seo) (tae-eo-nat-da) fK (<버락 오바마, 호놀룰루>) = 1 10
- 11. 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 11
- 12. 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) 12
- 13. 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-] <ARG1> was born in <ARG2> <ARG1> was born was born in born in <ARG2> 13
- 14. Graph Construction • Edge Weights Between instance node and context node in the same language 𝑤 𝑣 𝑖,𝑗 , 𝑢 𝑘 1 𝑖𝑓 𝑣 𝑖𝑗 ℎ𝑎𝑠 𝑢 𝑘 𝑎𝑠 𝑎 𝑐𝑜𝑛𝑡𝑒𝑥𝑡𝑢𝑎𝑙 𝑠𝑢𝑏𝑠𝑒𝑞𝑢𝑒𝑛𝑐𝑒, = 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒. Between context nodes in a language 𝑘, |𝑢 𝑘 ∩ 𝑢 𝑙 | 𝑤(𝑢 𝑢 𝑙) = 𝐽(𝑢 𝑘, 𝑢 𝑙) = 𝑘 . |𝑢 ∪ 𝑢 𝑙 | Between context nodes in source and target languages 𝑐𝑜𝑢𝑛𝑡 𝑢 𝑠𝑘 , 𝑢 𝑙𝑡 𝑤(𝑢 𝑠𝑘 , 𝑢 𝑙𝑡 ) = 𝑘 𝑚 , 𝑢𝑡 𝑚 𝑐𝑜𝑢𝑛𝑡 𝑢 𝑠 , 𝑢 𝑡 14
- 15. Graph Construction • Example 15
- 16. Label Propagation Initialize T • Algorithm Input • A transition matrix T • An initial label matrix Y0 Normalize T Output • The updated label matrix Yt Initialize Y Update Y 16
- 17. Label Propagation • Executed in three phases 1st phase 2nd phase 3rd phase 17
- 18. Contents • Introduction • Methods Cross-lingual Annotation Projection for Relation Extraction Graph-based Projection Approach • Evaluation • Conclusions 18
- 19. Implementation • Dataset English-Korean parallel corpus • 266,982 bi-sentence pairs in English and Korean • Aligned by GIZA++ • Annotation ReVerb (Fader et al., 2011) • English Open IE system • Label Propagation Junto Label Propagation Toolkit • Learning Tree kernel-based SVM classifier • Shortest path dependency kernel (Bunescu and Mooney, 2005) • SVM-Light (Joachims, 1998) 19
- 20. Evaluation • Dataset Manually annotated Korean dataset • Obtained from the Web following Bunescu and Mooney(2007)’s work • 500 sentences with manual annotations for four relation types Acquisition Birthplace Inventor Of Won Prize • Evaluation Metrics Precision/Recall/F-measure 20
- 21. Experimental Results • Direct Projection vs. Graph-based Projection Direct Projection Graph-based Projection Type P R F P R F Acquisition 51.6 87.7 64.9 55.3 91.2 68.9 Birthplace 69.8 84.5 76.4 73.8 87.3 80.0 Inventor of 62.4 85.3 72.1 66.3 89.7 76.3 Won Prize 73.3 80.5 76.7 76.4 82.9 79.5 Total 63.9 84.2 72.7 67.7 87.4 76.3 21
- 22. Experimental Results • Comparisons to other self-supervised approaches Heuristic-based Approach (Banko et al., 2007; Banko et al., 2008) • Korean Treebank and Syntactic Heuristics Wikipedia-based Approach (Wu and Weld, 2010) • Korean Wikipedia articles and Infoboxes Approach P R F Heuristic-based 92.31 17.27 29.09 Wikipedia-based 66.67 66.91 66.79 Projection-based 67.69 87.41 76.30 22
- 23. Contents • Introduction • Methods Cross-lingual Annotation Projection for Relation Extraction Graph-based Projection Approach • Evaluation • Conclusions 23
- 24. Conclusion • Summary A graph-based projection approach for relation extraction • Label propagation algorithm • On a graph that represents the instance and context features of both the source and target languages Experimental results show that our approach helps to improve the performances of relation extraction compared to other approaches • Future work To relieve the high complexity problem of the approach To deal with more expanded graph structure to improve the extraction performances 24
- 25. Q&A