---
title: "Multi-Task Identification of Entities — Luan 2018"
description: "Scientific literature keeps growing, so researchers need automatic ways to see which tasks, methods and datasets exist and how they connect."
authors: ["Yi Luan", "Luheng He", "Mari Ostendorf", "Hannaneh Hajishirzi"]
year: 2018
venue: "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D18-1360/"
doi: "10.18653/v1/d18-1360"
arxiv: "1808.09602"
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/luan-2018-multi-task-identification-entities-relations/"
---
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# Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

**Authors:** [Yi Luan](https://md.research.elamur.ai/free/en/authors/yi-luan/), [Luheng He](https://md.research.elamur.ai/free/en/authors/luheng-he/), [Mari Ostendorf](https://md.research.elamur.ai/free/en/authors/mari-ostendorf/), [Hannaneh Hajishirzi](https://md.research.elamur.ai/free/en/authors/hannaneh-hajishirzi/) · **Year:** 2018 · **Venue:** Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D18-1360/) · [PDF](https://aclanthology.org/D18-1360.pdf) · [DOI 10.18653/v1/d18-1360](https://doi.org/10.18653/v1/d18-1360) · [arXiv 1808.09602](https://arxiv.org/abs/1808.09602) · [OpenAlex](https://openalex.org/W2963718112)

**Citations:** 673 citations (openalex-snapshot · 2026-10-14) · [260](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Scientific literature keeps growing, so researchers need automatic ways to see which tasks, methods and datasets exist and how they connect. Information extraction (IE) in this domain is hard for two reasons. Expert annotation is costly, and many relations span sentence boundaries, often through vague mentions like "it" or "our method". The authors built SciERC, a corpus of 500 AI paper abstracts from the Semantic Scholar Corpus. It is jointly annotated for entities, relations and coreference clusters. They also propose SciIE, a multi-task model that extends span-based coreference and semantic role labeling architectures. SciIE enumerates all candidate spans, so it can handle overlapping spans. It treats entity typing, relation classification and coreference as classification problems over one shared span representation, with no hand-crafted features or pipeline steps. Joint training reduces error propagation between tasks and lets coreference carry information across sentences. SciIE beats earlier scientific IE systems on SciERC and SemEval 17. Applied to a large paper collection, it yields a knowledge graph that is denser and more useful when coreference links are propagated.

## Key points
- **SciERC dataset.** 500 abstracts from 12 AI venues spanning general AI, NLP, speech, ML and CV. It has 6 entity types (Task, Method, Metric, Material, Other-ScientificTerm, Generic) and 7 relation types, such as Used-for, Part-of and Compare.
- **Annotation scale and agreement.**
  - About 8,089 entities, 4,716 relations, 2,752 coreference links and 1,023 clusters.
  - This is 9.4 relations per abstract, against 3.2 for SemEval 18 and 1.3 for SemEval 17.
  - Kappa scores: 76.9% for entities, 67.8% for relations and 63.8% for coreference.
- **Multi-task ablation.** Adding coreference improves relation extraction by about 7.1% relative. Adding relation extraction improves coreference by about 4.9% relative.
- **SemEval 17 results.** SciIE outperforms feature-based state-of-the-art systems, with the largest gain in span identification. The authors attribute this to span enumeration rather than BIO tagging.
- **Knowledge graph evaluation.**
  - Four experts judged about 1.5k relation triples.
  - Precision was above 84% both with and without coreference.
  - With coreference, recall was clearly higher, raising AUC from 0.695 to 0.751.
- **Release.** Data and code are released at nlp.cs.washington.edu/sciIE.

*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: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/)

## Cited by

Cited by

260 papers of our library cite it (of the 140673 whose bibliographies we read); 17 of them have a page here, 243 not yet.

- [Do Lexical and Contextual Coreference Resolution Systems Degrade Differently under Mention Noise? An Empirical Study on Scientific Software Mentions](https://md.research.elamur.ai/free/en/papers/alkan-2026-do-lexical-contextual-coreference-resolution/)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://md.research.elamur.ai/free/en/papers/beltagy-2019-scibert-pretrained-language-model-scientific/)
- [What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text](https://md.research.elamur.ai/free/en/papers/fang-2022-what-does-it-take-bake/)
- [End-to-end Deep Reinforcement Learning Based Coreference Resolution](https://md.research.elamur.ai/free/en/papers/fei-2019-end-end-deep-reinforcement-learning/)
- [Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2](https://md.research.elamur.ai/free/en/papers/ivison-2023-camels-changing-climate-enhancing-lm/)
- [SciREX: A Challenge Dataset for Document-Level Information Extraction](https://md.research.elamur.ai/free/en/papers/jain-2020-scirex-challenge-dataset-document-level/)
- [Text Generation from Knowledge Graphs with Graph Transformers](https://md.research.elamur.ai/free/en/papers/kedziorski-2019-text-generation-knowledge-graphs-graph/)
- [S2ORC: The Semantic Scholar Open Research Corpus](https://md.research.elamur.ai/free/en/papers/lo-2019-s2orc-semantic-scholar-open-research/)
- [Merging Models with Fisher-Weighted Averaging](https://md.research.elamur.ai/free/en/papers/matena-2021-merging-models-fisher-weighted-averaging/)
- [F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://md.research.elamur.ai/free/en/papers/otmazgin-2022-f-coref-fast-accurate-easy/)
- [Investigating Pretrained Language Models for Graph-to-Text Generation](https://md.research.elamur.ai/free/en/papers/ribeiro-2021-investigating-pretrained-language-models-graph/)
- [Neural Modeling for Named Entities and Morphology (NEMO2)](https://md.research.elamur.ai/free/en/papers/tacl-2021-bareket-neural-modeling-named-entities-morphology/)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://md.research.elamur.ai/free/en/papers/wadden-2019-entity-relation-event-extraction-contextualized/)
- [Fact or Fiction: Verifying Scientific Claims](https://md.research.elamur.ai/free/en/papers/wadden-2020-fact-or-fiction-verifying-scientific/)
- [GPT-RE: In-context Learning for Relation Extraction using Large Language Models](https://md.research.elamur.ai/free/en/papers/wan-2023-gpt-re-context-learning-relation/)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://md.research.elamur.ai/free/en/papers/wang-2023-instructuie-multi-task-instruction-tuning/)
- [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{luan2018multi,
  title = {{Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction}},
  author = {Luan, Yi and He, Luheng and Ostendorf, Mari and Hajishirzi, Hannaneh},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1360},
  eprint = {1808.09602},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/d18-1360}
}
```

- [← previous Multi-task dialog act and sentiment recognition on Mastodon](https://md.research.elamur.ai/free/en/papers/cerisara-2018-multi-task-dialog-act-sentiment/)
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- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/3/)

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