--- title: "Exploring Various Knowledge in Relation — Zhou 2005" description: "The paper addresses relation extraction as defined in the ACE RDC task: given two entity mentions, decide whether a semantic relation links them and, if…" authors: ["GuoDong Zhou", "Jian Su", "Jie Zhang", "Min Zhang"] year: 2005 venue: "Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05)" source: "https://aclanthology.org/P05-1053/" doi: "10.3115/1219840.1219893" 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/zhou-2005-exploring-various-knowledge-relation-extraction/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhou-2005-exploring-various-knowledge-relation-extraction/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2005](https://md.research.elamur.ai/free/en/papers/2005/) / Exploring Various Knowledge in Relation Extraction # Exploring Various Knowledge in Relation Extraction **Authors:** [GuoDong Zhou](https://md.research.elamur.ai/free/en/authors/guodong-zhou/), [Jian Su](https://md.research.elamur.ai/free/en/authors/jian-su/), Jie Zhang, [Min Zhang](https://md.research.elamur.ai/free/en/authors/min-zhang/) · **Year:** 2005 · **Venue:** Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05) · **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/P05-1053/) · [PDF](https://aclanthology.org/P05-1053.pdf) · [DOI 10.3115/1219840.1219893](https://doi.org/10.3115/1219840.1219893) · [OpenAlex](https://openalex.org/W2053238041) **Citations:** 764 citations (openalex-snapshot · 2026-10-14) · [173](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary The paper addresses relation extraction as defined in the ACE RDC task: given two entity mentions, decide whether a semantic relation links them and, if so, assign one of 5 types or 24 subtypes. The authors build a feature-based classifier with SVMLight (linear kernel, one-vs-others decomposition). They systematically add lexical cues (words, head words, entity types, mention levels, overlap counts), syntactic cues (base phrase chunks, full parse trees, dependency trees) and semantic resources (WordNet and a Name List). A central design choice is to separate chunk-derived features from full-parse features so that each source's contribution can be measured on its own. Chunking turns out to supply most of the syntactic benefit, and full parsing adds little. The authors explain this by noting that most ACE relations join mentions that sit very close together. On the 24 subtypes the system beats the previous best feature-based system by roughly 3 F. On the 5 types it beats tree-kernel methods by over 20 F. The findings suggest that cheap shallow features can stand in for costly deep parsing. ## Key points - Setup: ACE corpus with 5 relation types and 24 subtypes, classified using SVMLight with a linear kernel and one-vs-others multi-class decomposition. Experiments assume perfect entity detection and tracking (EDT). - Results on the test set: - Relation detection reaches 84.8 / 66.7 / 74.7 (P / R / F). - Classification into the 5 types reaches 68.0 F. - Classification into the 24 subtypes reaches 55.5 F. - Baselines: Kambhatla (2004) scored 52.8 F on subtypes. The Culotta et al. (2004) tree kernel scored 63.2 F on detection and 45.8 F on types. - Relations are mostly local. Of 9,830 training relations, 4,163 have no words between the two mentions, and 7,694 have no other mention in between. - Error analysis: 73% of errors (627/864) come from detection rather than characterization, so detection is the main bottleneck. "AT" is singled out as a particularly hard type. ## Interesting ideas - **Separate feature families to attribute their value.** Splitting chunking features from full-parse features showed that the expensive component added little. The same ablation design could tell a low-resource language project, such as Ukrainian, whether a parser is worth the cost or a chunker is enough. - **Check the distance distribution before choosing deep syntax.** Counting the words and mentions between argument pairs explains why shallow features work. This simple corpus statistic can guide architecture decisions for any pairwise extraction task. - **Positional binning of context words.** Context words are split into slots: the first and last word between the mentions, the remaining in-between words, and the two words on each outer side. This keeps word order information within a linear model and transfers easily to other span-pair classification problems. - **Resolve pronouns before extracting features.** A pronominal mention is replaced by its nearest non-pronominal antecedent from the coreference chain, so uninformative words like "it" do not dilute the features. This is useful wherever arguments are often pronouns. - **Trim mention spans to reduce noise.** Each mention is cut from the start of its extent to the end of its head, which drops trailing modifiers such as "of McDonald". The idea is a cheap way to clean noisy argument spans in other annotation schemes. *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 173 papers of our library cite it (of the 140884 whose bibliographies we read); 13 of them have a page here, 160 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/) - [Knowledge Graphs](https://md.research.elamur.ai/free/en/papers/hogan-2020-knowledge-graphs/) - [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://md.research.elamur.ai/free/en/papers/krishna-2016-visual-genome-connecting-language-vision/) - [Incremental Joint Extraction of Entity Mentions and Relations](https://md.research.elamur.ai/free/en/papers/li-2014-incremental-joint-extraction-entity-mentions/) - [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/) - [Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://md.research.elamur.ai/free/en/papers/peng-2017-cross-sentence-n-ary-relation/) - [Relation Classification via Multi-Level Attention CNNs](https://md.research.elamur.ai/free/en/papers/wang-2016-relation-classification-multi-level-attention/) - [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/) - [Relation Classification via Convolutional Deep Neural Network](https://md.research.elamur.ai/free/en/papers/zeng-2014-relation-classification-convolutional-deep-neural/) - [A Frustratingly Easy Approach for Entity and Relation Extraction](https://md.research.elamur.ai/free/en/papers/zhong-2020-frustratingly-easy-approach-entity-relation/) - [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{zhou2005exploring, title = {{Exploring Various Knowledge in Relation Extraction}}, author = {Zhou, GuoDong and Su, Jian and Zhang, Jie and Zhang, Min}, booktitle = {Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05)}, year = {2005}, doi = {10.3115/1219840.1219893}, url = {https://doi.org/10.3115/1219840.1219893} } ``` - [← previous Evaluating Machine Translation Output with Automatic Sentence Segmentation](https://md.research.elamur.ai/free/en/papers/matusov-2005-evaluating-machine-translation-output-automatic/) - [next → Extracting Product Features and Opinions from Reviews](https://md.research.elamur.ai/free/en/papers/popescu-2005-extracting-product-features-opinions-reviews/) - [↑ back to papers of 2005](https://md.research.elamur.ai/free/en/papers/2005/) ## References 14 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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In Proceedings of 42 th Annual Meeting of the Association for Computational Linguistics. 21-26 July 2004. [find](https://scholar.google.com/scholar?q=Culotta+A%2E+and+Sorensen+J%2E+%282004%29%2E+Dependency+tree+kernels+for+relation+extraction%2E+In+Proceedings+of+42+th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2E+21%2D26+July+2004%2E) [our card](https://md.research.elamur.ai/free/en/papers/culotta-2004-dependency-tree-kernels-relation-extraction/) 5. Barcelona, Spain Cumby C.M. and Roth D. (2003). On kernel methods for relation learning. In Fawcett T. and Mishra N. editors. In Proceedings of 20 th International Conference on Machine Learning (ICML’2003). 21-24 Aug 2003. Washington D.C. USA. AAAI Press. 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