---
title: "Joint Extraction of Entities and Relations — Zheng 2017"
description: "The paper addresses joint extraction of entities and relations: finding entity pairs in raw text together with a relation type from a fixed inventory."
authors: ["Suncong Zheng", "Feng Wang", "Hongyun Bao", "Yuexing Hao", "Peng Zhou", "Bo Xu"]
year: 2017
venue: "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P17-1113/"
doi: "10.18653/v1/p17-1113"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zheng-2017-joint-extraction-entities-relations-based/"
---
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# Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

**Authors:** Suncong Zheng, Feng Wang, Hongyun Bao, Yuexing Hao, [Peng Zhou](https://md.research.elamur.ai/free/en/authors/peng-zhou/), [Bo Xu](https://md.research.elamur.ai/free/en/authors/bo-xu/) · **Year:** 2017 · **Venue:** Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P17-1113/) · [PDF](https://aclanthology.org/P17-1113.pdf) · [DOI 10.18653/v1/p17-1113](https://doi.org/10.18653/v1/p17-1113) · [OpenAlex](https://openalex.org/W2808142148)

**Citations:** 787 citations (openalex-snapshot · 2026-10-14) · [115](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses joint extraction of entities and relations: finding entity pairs in raw text together with a relation type from a fixed inventory. Pipelined systems handle NER and relation classification separately, so errors pass from one stage to the next. Earlier joint systems either depend on heavy feature engineering and external NLP tools or still predict entities and relations as separate outputs. The authors instead treat the whole task as sequence labeling. Each token's tag combines three things: its position inside an entity, a relation type, and the entity's role in the triplet. A tag sequence can then be decoded straight into (Entity1, RelationType, Entity2) triplets. On top of this scheme they compare several LSTM-based end-to-end taggers and propose LSTM-LSTM-Bias, which uses a Bi-LSTM encoder, an LSTM decoder, and a training loss that weights relation-bearing tags more heavily. Experiments use a public distantly supervised dataset released with CoType (Ren et al., 2017). Tagging-based methods outperform most pipelined and joint baselines, and LSTM-LSTM-Bias gives the best overall results. The work matters because it shows that triplet extraction can be framed as ordinary tagging, which avoids hand-built features.

## Key points
- **Tag structure.** A tag is either "O" or a combination of three parts:
  - a BIES position marker,
  - a relation type,
  - a role number ("1" for the first entity, "2" for the second).
  
  The tag set size is therefore 2·4·|R| + 1, where |R| is the number of relation types.
- **Decoding tags into triplets.** Entities that carry the same relation type are paired into a triplet. When several pairs share a type, entities are matched by proximity. Overlapping relations, where one entity appears in several triplets, are explicitly left out of scope.
- **Models compared.**
  - LSTM-CRF
  - LSTM-LSTM
  - LSTM-LSTM-Bias
  
  The LSTM decoder slightly beats the CRF decoder, which the authors attribute to related tags often lying far apart in the sentence.
- **Bias parameter α.** α was varied from 1 to 20. Large values hurt precision and small values hurt recall. The best F1 was reached at α = 10.
- **Error analysis.**
  - LSTM-LSTM-Bias leaves the fewest unpaired single entities.
  - Case studies show remaining failures: misleading surface patterns, and entity pairs found correctly but with their roles swapped.

## Interesting ideas
- **Encoding a structured output as per-token labels.** Packing relation type and argument role into the tag lets a standard sequence tagger produce relational output. The same trick could help with other structured extraction tasks, such as trade-document fields or geographic relations, without designing a new architecture.
- **Class-weighted loss for rare informative tags.** Raising the loss weight on non-"O" tags counters the dominance of the "O" class. The single α knob gives explicit control over the precision–recall trade-off, and the same idea suits any sparse tagging task.
- **Checking whether outputs are complete.** Measuring the share of predicted entities that never found a partner is a cheap diagnostic. It shows whether a joint model actually connects arguments, beyond what P/R/F reports.
- **Using distant supervision to get training data.** Training on an automatically labeled, distantly supervised dataset avoids costly manual annotation. This is relevant for lower-resource languages such as Ukrainian, where annotated relation corpora are scarce.

*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/)

Topics: [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/)

## Cited by

Cited by

115 papers of our library cite it (of the 140884 whose bibliographies we read); 7 of them have a page here, 108 not yet.

- [Knowledge Graphs](https://md.research.elamur.ai/free/en/papers/hogan-2020-knowledge-graphs/)
- [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/)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://md.research.elamur.ai/free/en/papers/luan-2018-multi-task-identification-entities-relations/)
- [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/)
- [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/)
- [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/)
- [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{zheng2017joint,
  title = {{Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme}},
  author = {Zheng, Suncong and Wang, Feng and Bao, Hongyun and Hao, Yuexing and Zhou, Peng and Xu, Bo},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2017},
  doi = {10.18653/v1/p17-1113},
  url = {https://doi.org/10.18653/v1/p17-1113}
}
```

- [← previous Integrating syntactic theory and variationist analysis: The structure of negative indefinites in regional dialects of British English](https://md.research.elamur.ai/free/en/papers/childs-2017-integrating-syntactic-theory-variationist-analysis/)
- [next → Joint Prediction of Morphosyntactic Categories for Fine-Grained Arabic Part-of-Speech Tagging Exploiting Tag Dictionary Information](https://md.research.elamur.ai/free/en/papers/lingbulk-2017-inoue-joint-prediction-morphosyntactic-categories-fine/)
- [↑ back to papers of 2017](https://md.research.elamur.ai/free/en/papers/2017/page/2/)

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