---
title: "Modeling Joint Entity and Relation Extraction — Miwa 2014"
description: "Miwa and Sasaki tackle the joint extraction of typed, possibly multi-word entities and directed, typed relations within a single sentence."
authors: ["Makoto Miwa", "Yutaka Sasaki"]
year: 2014
venue: "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/D14-1200/"
doi: "10.3115/v1/d14-1200"
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/miwa-2014-modeling-joint-entity-relation-extraction/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/miwa-2014-modeling-joint-entity-relation-extraction/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2014](https://md.research.elamur.ai/free/en/papers/2014/page/2/) / Modeling Joint Entity and Relation Extraction with Table Representation

# Modeling Joint Entity and Relation Extraction with Table Representation

**Authors:** [Makoto Miwa](https://md.research.elamur.ai/free/en/authors/makoto-miwa/), Yutaka Sasaki · **Year:** 2014 · **Venue:** Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · **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/D14-1200/) · [PDF](https://aclanthology.org/D14-1200.pdf) · [DOI 10.3115/v1/d14-1200](https://doi.org/10.3115/v1/d14-1200) · [OpenAlex](https://openalex.org/W2251091211)

**Citations:** 369 citations (openalex-snapshot · 2026-10-14) · [74](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Miwa and Sasaki tackle the joint extraction of typed, possibly multi-word entities and directed, typed relations within a single sentence. Pipeline systems let entity errors propagate into relation extraction and treat each entity pair independently. The authors encode a whole sentence as the lower triangle of a word-by-word table. Diagonal cells hold BILOU entity tags, and off-diagonal cells hold relation labels with a direction (or ⊥ for no relation), attached to the last words of entities. Extraction becomes a table-filling problem, which they solve with history-based structured learning and beam search. Each cell is scored with local features plus global features that depend on earlier assignments, and hard label-dependency constraints rule out inconsistent fillings. They compare several learning algorithms and many static and dynamic (easy-first) cell orderings. On a corpus with PER/LOC/ORG entities and five relation types, the best joint configurations clearly beat a pipeline on combined entity+relation F1. The work shows that a simple representation can match more complex joint models and makes search order something one can tune.

## Key points
- The table for an n-word sentence has n(n+1)/2 cells, using only the lower triangle because the table is symmetric.
  - Entities use BILOU tags.
  - Relations use type plus →/← direction, or ⊥ for no relation.
- On the test set, the pipeline reaches 0.577 entity+relation F1.
  - The default joint model reaches 0.610, a significant improvement (p\<0.05). It uses DCD-SSVM, local+global features and an entity-first easy-first order.
  - The Perceptron and the close-first left-to-right order each reach 0.612.
- Global features matter a lot:
  - With local features only, combined F1 is 0.522.
  - Removing the relation-to-entity global features gives 0.568.
- Learning method and search order both change results substantially.
  - AROW gives 0.561 and AdaGrad 0.572.
  - Static orders range from 0.540 to 0.612.
  - Naive easy-first gives 0.540.
- With 5-fold cross-validation and entity boundaries given, the model reaches 0.924 F1 on entities and 0.698 on relations. This beats Kate and Mooney (2010) and Roth and Yih (2007), although the comparison is not strictly direct. Adding boundary detection costs about 0.09 F1.

## Interesting ideas
- **One table for both tasks.** Putting entity tags on the diagonal and pairwise relation labels off the diagonal turns two coupled tasks into one labeling problem. The same trick could suit any task mixing per-token and per-pair decisions, such as coreference or dependency-like annotation.
- **Anchoring relations to words.** Relations are attached to the last word of each entity rather than to whole entity spans. This means relation decisions do not have to wait until entity boundaries are final, which keeps joint decoding simple.
- **Hard constraints from the tag scheme.** The authors derive which labels are still allowed from already-filled cells, for example BILOU continuity or "a relation implies the word ends an entity." This blocks illegal outputs and also shrinks the beam's search space, so it is cheap to adopt in other constrained decoders.
- **Search order as a tunable design choice.** Treating the cell-visiting order as a hyperparameter can change results noticeably, whether the order is forward, backward, close-first or easy-first mixed with entity-first. This is worth testing in any greedy or beam-based structured predictor.
- **Ablating global features by direction.** Removing only the features that pass information from relations back to entities isolates the benefit of each direction of interaction. This evaluation design transfers well to other joint models.

*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

74 papers of our library cite it (of the 140884 whose bibliographies we read); 7 of them have a page here, 67 not yet.

- [A Joint Neural Model for Information Extraction with Global Features](https://md.research.elamur.ai/free/en/papers/lin-2020-joint-neural-model-information-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/)
- [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/)
- [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://md.research.elamur.ai/free/en/papers/zheng-2017-joint-extraction-entities-relations-based/)
- [A Frustratingly Easy Approach for Entity and Relation Extraction](https://md.research.elamur.ai/free/en/papers/zhong-2020-frustratingly-easy-approach-entity-relation/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{miwa2014modeling,
  title = {{Modeling Joint Entity and Relation Extraction with Table Representation}},
  author = {Miwa, Makoto and Sasaki, Yutaka},
  booktitle = {Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2014},
  doi = {10.3115/v1/d14-1200},
  url = {https://doi.org/10.3115/v1/d14-1200}
}
```

- [← previous Modeling Biological Processes for Reading Comprehension](https://md.research.elamur.ai/free/en/papers/berant-2014-modeling-biological-processes-reading-comprehension/)
- [next → Modelling Irony in Twitter](https://md.research.elamur.ai/free/en/papers/barbieri-2014-modelling-irony-twitter/)
- [↑ back to papers of 2014](https://md.research.elamur.ai/free/en/papers/2014/page/2/)

## References

31 references, 12 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Ming-Wei Chang and Wen-Tau Yih. 2013. Dual coordinate descent algorithms for efficient large margin structured prediction. Transactions of the Association for Computational Linguistics, 1:207–218. [find](https://scholar.google.com/scholar?q=Ming%2DWei+Chang+and+Wen%2DTau+Yih%2E+2013%2E+Dual+coordinate+descent+algorithms+for+efficient+large+margin+structured+prediction%2E+Transactions+of+the+Association+for+Computational+Linguistics%2C+1%3A207%E2%80%93218%2E)
2. Michael Collins and Brian Roark. 2004. Incremental parsing with the perceptron algorithm. In Proceedings of the 42nd Meeting of the Association for Computational Linguistics (ACL’04), Main Volume, pages 111–118, Barcelona, Spain, July. [find](https://scholar.google.com/scholar?q=Michael+Collins+and+Brian+Roark%2E+2004%2E+Incremental+parsing+with+the+perceptron+algorithm%2E+In+Proceedings+of+the+42nd+Meeting+of+the+Association+for+Computational+Linguistics+%28ACL%E2%80%9904%29%2C+Main+Volume%2C+pag) [our card](https://md.research.elamur.ai/free/en/papers/collins-2004-incremental-parsing-perceptron-algorithm/)
3. Michael Collins. 2002. Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, pages 1–8. Association for Computational Linguistics, July. [find](https://scholar.google.com/scholar?q=Michael+Collins%2E+2002%2E+Discriminative+training+methods+for+hidden+markov+models%3A+Theory+and+experiments+with+perceptron+algorithms%2E+In+Proceedings+of+the+2002+Conference+on+Empirical+Methods+in+Natura) [our card](https://md.research.elamur.ai/free/en/papers/collins-2002-discriminative-training-methods-hidden-markov/)
4. Koby Crammer, Alex Kulesza, and Mark Dredze. 2013. Adaptive regularization of weight vectors. Machine learning, 91(2):155–187. [find](https://scholar.google.com/scholar?q=Koby+Crammer%2C+Alex+Kulesza%2C+and+Mark+Dredze%2E+2013%2E+Adaptive+regularization+of+weight+vectors%2E+Machine+learning%2C+91%282%29%3A155%E2%80%93187%2E)
5. John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 12:2121–2159. [find](https://scholar.google.com/scholar?q=John+Duchi%2C+Elad+Hazan%2C+and+Yoram+Singer%2E+2011%2E+Adaptive+subgradient+methods+for+online+learning+and+stochastic+optimization%2E+The+Journal+of+Machine+Learning+Research%2C+12%3A2121%E2%80%932159%2E)
6. Radu Florian, Abe Ittycheriah, Hongyan Jing, and Tong Zhang. 2003. Named entity recognition through classifier combination. In Walter Daelemans and Miles Osborne, editors, Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, pages 168–171. [find](https://scholar.google.com/scholar?q=Radu+Florian%2C+Abe+Ittycheriah%2C+Hongyan+Jing%2C+and+Tong+Zhang%2E+2003%2E+Named+entity+recognition+through+classifier+combination%2E+In+Walter+Daelemans+and+Miles+Osborne%2C+editors%2C+Proceedings+of+the+Seventh+C) [our card](https://md.research.elamur.ai/free/en/papers/florian-2003-named-entity-recognition-through-classifier/)
7. Yoav Freund and Robert E Schapire. 1999. Large margin classification using the perceptron algorithm. Machine learning, 37(3):277–296. [find](https://scholar.google.com/scholar?q=Yoav+Freund+and+Robert+E+Schapire%2E+1999%2E+Large+margin+classification+using+the+perceptron+algorithm%2E+Machine+learning%2C+37%283%29%3A277%E2%80%93296%2E)
8. Yoav Goldberg and Michael Elhadad. 2010. An efficient algorithm for easy-first non-directional dependency parsing. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 742–750, Los Angeles, California, June.… [find](https://scholar.google.com/scholar?q=Yoav+Goldberg+and+Michael+Elhadad%2E+2010%2E+An+efficient+algorithm+for+easy%2Dfirst+non%2Ddirectional+dependency+parsing%2E+In+Human+Language+Technologies%3A+The+2010+Annual+Conference+of+the+North+American+Chap)
9. Liang Huang, Suphan Fayong, and Yang Guo. 2012. Structured perceptron with inexact search. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 142–151, Montréal, Canada, June. Association for Comput… [find](https://scholar.google.com/scholar?q=Liang+Huang%2C+Suphan+Fayong%2C+and+Yang+Guo%2E+2012%2E+Structured+perceptron+with+inexact+search%2E+In+Proceedings+of+the+2012+Conference+of+the+North+American+Chapter+of+the+Association+for+Computational+Ling) [our card](https://md.research.elamur.ai/free/en/papers/huang-2012-structured-perceptron-inexact-search/)
10. Rohit J. Kate and Raymond Mooney. 2010. Joint entity and relation extraction using card-pyramid parsing. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pages 203–212, Uppsala, Sweden, July. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Rohit+J%2E+Kate+and+Raymond+Mooney%2E+2010%2E+Joint+entity+and+relation+extraction+using+card%2Dpyramid+parsing%2E+In+Proceedings+of+the+Fourteenth+Conference+on+Computational+Natural+Language+Learning%2C+pages+2)
11. Taku Kudo and Yuji Matsumoto. 2001. Chunking with support vector machines. In Proceedings of the Second Meeting of the North American Chapter of the Association for Computational Linguistics on Language Technologies, Stroudsburg, PA, USA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Taku+Kudo+and+Yuji+Matsumoto%2E+2001%2E+Chunking+with+support+vector+machines%2E+In+Proceedings+of+the+Second+Meeting+of+the+North+American+Chapter+of+the+Association+for+Computational+Linguistics+on+Langua) [our card](https://md.research.elamur.ai/free/en/papers/kudo-2001-chunking-support-vector-machines/)
12. Qi Li and Heng Ji. 2014. Incremental joint extraction of entity mentions and relations. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 402–412, Baltimore, Maryland, June. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Qi+Li+and+Heng+Ji%2E+2014%2E+Incremental+joint+extraction+of+entity+mentions+and+relations%2E+In+Proceedings+of+the+52nd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+1%3A+Long+Paper) [our card](https://md.research.elamur.ai/free/en/papers/li-2014-incremental-joint-extraction-entity-mentions/)
13. Ji Ma, Tong Xiao, Jingbo Zhu, and Feiliang Ren. 2012. Easy-first Chinese POS tagging and dependency parsing. In Proceedings of COLING 2012, pages 1731–1746, Mumbai, India, December. [find](https://scholar.google.com/scholar?q=Ji+Ma%2C+Tong+Xiao%2C+Jingbo+Zhu%2C+and+Feiliang+Ren%2E+2012%2E+Easy%2Dfirst+Chinese+POS+tagging+and+dependency+parsing%2E+In+Proceedings+of+COLING+2012%2C+pages+1731%E2%80%931746%2C+Mumbai%2C+India%2C+December%2E)
14. The COLING 2012 Organizing Committee. [find](https://scholar.google.com/scholar?q=The+COLING+2012+Organizing+Committee%2E)
15. Avihai Mejer and Koby Crammer. 2010. Confidence in structured-prediction using confidence-weighted models. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 971–981, Cambridge, MA, October. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Avihai+Mejer+and+Koby+Crammer%2E+2010%2E+Confidence+in+structured%2Dprediction+using+confidence%2Dweighted+models%2E+In+Proceedings+of+the+2010+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pa)
16. Makoto Miwa, Rune Sætre, Yusuke Miyao, and Jun’ichi Tsujii. 2009. A rich feature vector for protein-protein interaction extraction from multiple corpora. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 121–130, Singapore, August. Association for Compu… [find](https://scholar.google.com/scholar?q=Makoto+Miwa%2C+Rune+S%C3%A6tre%2C+Yusuke+Miyao%2C+and+Jun%E2%80%99ichi+Tsujii%2E+2009%2E+A+rich+feature+vector+for+protein%2Dprotein+interaction+extraction+from+multiple+corpora%2E+In+Proceedings+of+the+2009+Conference+on+Empir)
17. Yusuke Miyao and Jun’ichi Tsujii. 2008. Feature forest models for probabilistic HPSG parsing. Computational Linguistics, 34(1):35–80, March. [find](https://scholar.google.com/scholar?q=Yusuke+Miyao+and+Jun%E2%80%99ichi+Tsujii%2E+2008%2E+Feature+forest+models+for+probabilistic+HPSG+parsing%2E+Computational+Linguistics%2C+34%281%29%3A35%E2%80%9380%2C+March%2E) [our card](https://md.research.elamur.ai/free/en/papers/cl-2008-miyao-feature-forest-models-probabilistic-hpsg/)
18. David Nadeau and Satoshi Sekine. 2007. A survey of named entity recognition and classification. Lingvisticae Investigationes, 30(1):3–26. [find](https://scholar.google.com/scholar?q=David+Nadeau+and+Satoshi+Sekine%2E+2007%2E+A+survey+of+named+entity+recognition+and+classification%2E+Lingvisticae+Investigationes%2C+30%281%29%3A3%E2%80%9326%2E)
19. Eric W. Noreen. 1989. Computer-Intensive Methods for Testing Hypotheses : An Introduction. Wiley-Interscience, April. [find](https://scholar.google.com/scholar?q=Eric+W%2E+Noreen%2E+1989%2E+Computer%2DIntensive+Methods+for+Testing+Hypotheses+%3A+An+Introduction%2E+Wiley%2DInterscience%2C+April%2E)
20. Lev Ratinov and Dan Roth. 2009. Design challenges and misconceptions in named entity recognition. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009), pages 147–155, Boulder, Colorado, June. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Lev+Ratinov+and+Dan+Roth%2E+2009%2E+Design+challenges+and+misconceptions+in+named+entity+recognition%2E+In+Proceedings+of+the+Thirteenth+Conference+on+Computational+Natural+Language+Learning+%28CoNLL%2D2009%29%2C+p) [our card](https://md.research.elamur.ai/free/en/papers/ratinov-2009-design-challenges-misconceptions-named-entity/)
21. Dan Roth and Wen-Tau Yih. 2004. A linear programming formulation for global inference in natural language tasks. In Hwee Tou Ng and Ellen Riloff, editors, HLT-NAACL 2004 Workshop: Eighth Conference on Computational Natural Language Learning (CoNLL-2004), pages 1–8, Boston, Massachusetts, USA, May. A… [find](https://scholar.google.com/scholar?q=Dan+Roth+and+Wen%2DTau+Yih%2E+2004%2E+A+linear+programming+formulation+for+global+inference+in+natural+language+tasks%2E+In+Hwee+Tou+Ng+and+Ellen+Riloff%2C+editors%2C+HLT%2DNAACL+2004+Workshop%3A+Eighth+Conference+on) [our card](https://md.research.elamur.ai/free/en/papers/roth-2004-linear-programming-formulation-global-inference/)
22. Dan Roth and Wen-Tau Yih, 2007. Global Inference for Entity and Relation Identification via a Linear Programming Formulation. MIT Press. [find](https://scholar.google.com/scholar?q=Dan+Roth+and+Wen%2DTau+Yih%2C+2007%2E+Global+Inference+for+Entity+and+Relation+Identification+via+a+Linear+Programming+Formulation%2E+MIT+Press%2E)
23. Kenji Sagae and Jun’ichi Tsujii. 2007. Dependency parsing and domain adaptation with LR models and parser ensembles. In Proceedings of the CoNLL Shared Task Session of EMNLP-CoNLL 2007, pages 1044–1050, Prague, Czech Republic, June. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Kenji+Sagae+and+Jun%E2%80%99ichi+Tsujii%2E+2007%2E+Dependency+parsing+and+domain+adaptation+with+LR+models+and+parser+ensembles%2E+In+Proceedings+of+the+CoNLL+Shared+Task+Session+of+EMNLP%2DCoNLL+2007%2C+pages+1044%E2%80%93105) [our card](https://md.research.elamur.ai/free/en/papers/sagae-2007-dependency-parsing-domain-adaptation-lr/)
24. Sameer Singh, Sebastian Riedel, Brian Martin, Jiaping Zheng, and Andrew McCallum. 2013. Joint inference of entities, relations, and coreference. In Proceedings of the 2013 workshop on Automated knowledge base construction, pages 1–6. ACM. [find](https://scholar.google.com/scholar?q=Sameer+Singh%2C+Sebastian+Riedel%2C+Brian+Martin%2C+Jiaping+Zheng%2C+and+Andrew+McCallum%2E+2013%2E+Joint+inference+of+entities%2C+relations%2C+and+coreference%2E+In+Proceedings+of+the+2013+workshop+on+Automated+knowle)
25. Veselin Stoyanov and Jason Eisner. 2012. Easy-first coreference resolution. In Proceedings of COLING 2012, pages 2519–2534, Mumbai, India, December. [find](https://scholar.google.com/scholar?q=Veselin+Stoyanov+and+Jason+Eisner%2E+2012%2E+Easy%2Dfirst+coreference+resolution%2E+In+Proceedings+of+COLING+2012%2C+pages+2519%E2%80%932534%2C+Mumbai%2C+India%2C+December%2E) [our card](https://md.research.elamur.ai/free/en/papers/stoyanov-2012-easy-first-coreference-resolution/)
26. The COLING 2012 Organizing Committee. [find](https://scholar.google.com/scholar?q=The+COLING+2012+Organizing+Committee%2E)
27. Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, and Josh Attenberg. 2009. Feature hashing for large scale multitask learning. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML ’09, pages 1113– 1120, New York, NY, USA. ACM. [find](https://scholar.google.com/scholar?q=Kilian+Weinberger%2C+Anirban+Dasgupta%2C+John+Langford%2C+Alex+Smola%2C+and+Josh+Attenberg%2E+2009%2E+Feature+hashing+for+large+scale+multitask+learning%2E+In+Proceedings+of+the+26th+Annual+International+Conference)
28. Bishan Yang and Claire Cardie. 2013. Joint inference for fine-grained opinion extraction. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1640–1649, Sofia, Bulgaria, August. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Bishan+Yang+and+Claire+Cardie%2E+2013%2E+Joint+inference+for+fine%2Dgrained+opinion+extraction%2E+In+Proceedings+of+the+51st+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+1%3A+Long+Pap)
29. Xiaofeng Yu and Wai Lam. 2010. Jointly identifying entities and extracting relations in encyclopedia text via a graphical model approach. In Coling 2010: Posters, pages 1399–1407, Beijing, China, August. [find](https://scholar.google.com/scholar?q=Xiaofeng+Yu+and+Wai+Lam%2E+2010%2E+Jointly+identifying+entities+and+extracting+relations+in+encyclopedia+text+via+a+graphical+model+approach%2E+In+Coling+2010%3A+Posters%2C+pages+1399%E2%80%931407%2C+Beijing%2C+China%2C+Augu)
30. Coling 2010 Organizing Committee. [find](https://scholar.google.com/scholar?q=Coling+2010+Organizing+Committee%2E)
31. Dmitry Zelenko, Chinatsu Aone, and Anthony Richardella. 2003. Kernel methods for relation extraction. The Journal of Machine Learning Research, 3:1083–1106. [find](https://scholar.google.com/scholar?q=Dmitry+Zelenko%2C+Chinatsu+Aone%2C+and+Anthony+Richardella%2E+2003%2E+Kernel+methods+for+relation+extraction%2E+The+Journal+of+Machine+Learning+Research%2C+3%3A1083%E2%80%931106%2E) [our card](https://md.research.elamur.ai/free/en/papers/zelenko-2002-kernel-methods-relation-extraction/)