--- title: "Kernel Methods for Relation Extraction — Zelenko 2002" description: "The paper addresses relation extraction: deciding whether a semantic relation holds between two entity mentions in text." authors: ["Dmitry Zelenko", "Chinatsu Aone", "Anthony Richardella"] year: 2002 venue: "Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)" source: "https://aclanthology.org/W02-1010/" doi: "10.3115/1118693.1118703" licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/zelenko-2002-kernel-methods-relation-extraction/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zelenko-2002-kernel-methods-relation-extraction/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2002](https://md.research.elamur.ai/free/en/papers/2002/) / Kernel Methods for Relation Extraction # Kernel Methods for Relation Extraction **Authors:** [Dmitry Zelenko](https://md.research.elamur.ai/free/en/authors/dmitry-zelenko/), [Chinatsu Aone](https://md.research.elamur.ai/free/en/authors/chinatsu-aone/), Anthony Richardella · **Year:** 2002 · **Venue:** Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/) **Links:** [Publisher's page](https://aclanthology.org/W02-1010/) · [PDF](https://aclanthology.org/W02-1010.pdf) · [DOI 10.3115/1118693.1118703](https://doi.org/10.3115/1118693.1118703) · [OpenAlex](https://openalex.org/W2162590473) **Citations:** 573 citations (openalex-snapshot · 2026-10-14) · [256](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary The paper addresses relation extraction: deciding whether a semantic relation holds between two entity mentions in text. The authors frame it as classification over structured objects. Instead of turning sentences into hand-built feature vectors, they define kernels that compare shallow parse trees directly. Each candidate relation is a piece of a shallow parse whose nodes carry attributes such as phrase type, text and entity role. Similarity is computed by recursively matching nodes and their child sequences. Two variants are proposed. The contiguous subtree kernel counts only adjacent child subsequences, while the sparse kernel also allows gapped ones, down-weighted by a decay factor. Both are computed efficiently with dynamic programming. The kernels are plugged into Support Vector Machines and the Voted Perceptron. They are tested on person-affiliation and organization-location relations in news text, against feature-based learners. The kernel approaches beat the feature-based baselines. This matters because it shows that structural similarity can replace much manual feature engineering in information extraction. ## Key points - Two target relations: **person-affiliation** and **organization-location**, annotated in a corpus of news articles. - Examples are built from **shallow parses**. Each node has a type and a role (for example, which entity argument it represents). - Two tree kernels are introduced: - **contiguous subtree kernel**, which matches adjacent child subsequences only; - **sparse subtree kernel**, which matches gapped subsequences penalised by a decay parameter λ. - Both kernels run in polynomial time via dynamic programming. - **Learners:** SVM and Voted Perceptron with the new kernels. - **Baselines:** Naive Bayes, Winnow and a feature-vector SVM. - **Results:** kernel-based models gave higher F-measure than the feature-based ones, and the sparse kernel with SVM was generally strongest. - **Caveat:** the supplied PDF extraction is largely unreadable, so exact corpus sizes and scores are omitted here. Check them against the original paper. ## Interesting ideas - **Compare structures instead of engineering features.** A kernel over parse fragments avoids building huge explicit feature spaces. This is attractive for under-resourced languages like Ukrainian, where good hand-crafted features are scarce. - **Gapped matching with a decay penalty.** The sparse kernel tolerates inserted modifiers or extra words between matching elements while still preferring tight matches. The same trick suits noisy or morphologically rich text. - **Shallow parsing is enough.** Using chunk-level structure rather than full parses keeps the pipeline cheap and robust. This is a sensible design choice when a strong treebank-trained parser is unavailable. - **Entity roles as node attributes.** Marking which node plays which argument lets one generic kernel serve different relation types. It is a reusable pattern for multi-relation extraction, for example in trade or geography data. - **Keep the learner separate from the similarity function.** The same kernel works with both SVM and Voted Perceptron. Comparing them against feature-based baselines cleanly isolates the contribution of the representation. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) ## Cited by Cited by 256 papers of our library cite it (of the 140884 whose bibliographies we read); 27 of them have a page here, 229 not yet. - [Frame Semantic Tree Kernels for Social Network Extraction from Text](https://md.research.elamur.ai/free/en/papers/agarwal-2014-frame-semantic-tree-kernels-social/) - [A Shortest Path Dependency Kernel for Relation Extraction](https://md.research.elamur.ai/free/en/papers/bunescu-2005-shortest-path-dependency-kernel-relation/) - [Dependency Tree Kernels for Relation Extraction](https://md.research.elamur.ai/free/en/papers/culotta-2004-dependency-tree-kernels-relation-extraction/) - [Attention Guided Graph Convolutional Networks for Relation Extraction](https://md.research.elamur.ai/free/en/papers/guo-2019-attention-guided-graph-convolutional-networks/) - [Discovering Relations among Named Entities from Large Corpora](https://md.research.elamur.ai/free/en/papers/hasegawa-2004-discovering-relations-among-named-entities/) - [Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions](https://md.research.elamur.ai/free/en/papers/heilman-2010-tree-edit-models-recognizing-textual/) - [Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction](https://md.research.elamur.ai/free/en/papers/jie-2022-learning-reason-deductively-math-word/) - [Dependency-driven Anaphoricity Determination for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/kong-2010-dependency-driven-anaphoricity-determination-coreference/) - [A Tree Kernel-Based Unified Framework for Chinese Zero Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/kong-2010-tree-kernel-based-unified-framework/) - [Modeling Joint Entity and Relation Extraction with Table Representation](https://md.research.elamur.ai/free/en/papers/miwa-2014-modeling-joint-entity-relation-extraction/) - [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://md.research.elamur.ai/free/en/papers/miwa-2016-end-end-relation-extraction-lstms/) - [Relation Extraction: Perspective from Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/nguyen-2015-relation-extraction-perspective-convolutional-neural/) - [Similarity of Semantic Relations](https://md.research.elamur.ai/free/en/papers/turney-2006-similarity-semantic-relations/) - [Coreference Systems Based on Kernels Methods](https://md.research.elamur.ai/free/en/papers/versley-2008-coreference-systems-based-kernels-methods/) - [Kernel Based Discourse Relation Recognition with Temporal Ordering Information](https://md.research.elamur.ai/free/en/papers/wang-2010-kernel-based-discourse-relation-recognition/) - [TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking](https://md.research.elamur.ai/free/en/papers/wang-2020-tplinker-single-stage-joint-extraction/) - [Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders](https://md.research.elamur.ai/free/en/papers/wang-2020-two-are-better-than-one/) - [A Novel Cascade Binary Tagging Framework for Relational Triple Extraction](https://md.research.elamur.ai/free/en/papers/wei-2020-novel-cascade-binary-tagging-framework/) - [Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths](https://md.research.elamur.ai/free/en/papers/xu-2015-classifying-relations-long-short-term/) - [Kernel-Based Pronoun Resolution with Structured Syntactic Knowledge](https://md.research.elamur.ai/free/en/papers/yang-2006-kernel-based-pronoun-resolution-structured/) - [Relation Classification via Convolutional Deep Neural Network](https://md.research.elamur.ai/free/en/papers/zeng-2014-relation-classification-convolutional-deep-neural/) - [Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism](https://md.research.elamur.ai/free/en/papers/zeng-2018-extracting-relational-facts-end-end/) - [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://md.research.elamur.ai/free/en/papers/zhang-2018-graph-convolution-over-pruned-dependency/) - [知识图谱嵌入技术研究综述](https://md.research.elamur.ai/free/en/papers/zhang-2021-overview-knowledge-graph/) - [A Frustratingly Easy Approach for Entity and Relation Extraction](https://md.research.elamur.ai/free/en/papers/zhong-2020-frustratingly-easy-approach-entity-relation/) - [Exploring Various Knowledge in Relation Extraction](https://md.research.elamur.ai/free/en/papers/zhou-2005-exploring-various-knowledge-relation-extraction/) - [Context-Sensitive Convolution Tree Kernel for Pronoun Resolution](https://md.research.elamur.ai/free/en/papers/zhou-2008-context-sensitive-convolution-tree-kernel/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{zelenko2002kernel, title = {{Kernel Methods for Relation Extraction}}, author = {Zelenko, Dmitry and Aone, Chinatsu and Richardella, Anthony}, booktitle = {Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)}, year = {2002}, doi = {10.3115/1118693.1118703}, url = {https://doi.org/10.3115/1118693.1118703} } ``` - [← previous Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/sang-2002-introduction-conll-2002-shared-task/) - [next → Learning a Translation Lexicon from Monolingual Corpora](https://md.research.elamur.ai/free/en/papers/koehn-2002-learning-translation-lexicon-monolingual-corpora/) - [↑ back to papers of 2002](https://md.research.elamur.ai/free/en/papers/2002/)