# Improving Coreference Resolution by Learning Entity-Level Distributed Representations

arXiv: https://arxiv.org/abs/1606.01323 · HTML: https://mlsanity.com/paper/1606.01323/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2016.

## Built on by (most cited first, 30 of 50)

- [VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution](https://mlsanity.com/paper/2306.12424/) (2023)
- [LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution](https://mlsanity.com/paper/2205.12644/) (2022)
- [ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs](https://mlsanity.com/paper/2112.07622/) (2021)
- [Moving on from OntoNotes: Coreference Resolution Model Transfer](https://mlsanity.com/paper/2104.08457/) (2021)
- [OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres](https://mlsanity.com/paper/2106.00933/) (2021)
- [Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble](https://mlsanity.com/paper/2212.06522/) (2022)
- [CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues](https://mlsanity.com/paper/2105.09914/) (2021)
- [DocAMR: Multi-Sentence AMR Representation and Evaluation](https://mlsanity.com/paper/2112.08513/) (2021)
- [Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting](https://mlsanity.com/paper/2307.00866/) (2023)
- [A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends](https://mlsanity.com/paper/2302.03512/) (2023)
- [WebIE: Faithful and Robust Information Extraction on the Web](https://mlsanity.com/paper/2305.14293/) (2023)
- [Graph Refinement for Coreference Resolution](https://mlsanity.com/paper/2203.16574/) (2022)
- [Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition](https://mlsanity.com/paper/2204.05544/) (2022)
- [Interactive Question Answering Systems: Literature Review](https://mlsanity.com/paper/2209.01621/) (2022)
- [Who are you referring to? Coreference resolution in image narrations](https://mlsanity.com/paper/2211.14563/) (2022)
- [XCoref: Cross-document Coreference Resolution in the Wild](https://mlsanity.com/paper/2109.05252/) (2021)
- [Detecting and Grounding Important Characters in Visual Stories](https://mlsanity.com/paper/2303.17647/) (2023)
- [Adapted End-to-End Coreference Resolution System for Anaphoric Identities in Dialogues](https://mlsanity.com/paper/2109.00185/) (2021)
- [Proto: A Neural Cocktail for Generating Appealing Conversations](https://mlsanity.com/paper/2109.02513/) (2021)
- [Transformers as Graph-to-Graph Models](https://mlsanity.com/paper/2310.17936/) (2023)
- [Scoring Coreference Chains with Split-Antecedent Anaphors](https://mlsanity.com/paper/2205.12323/) (2022)
- [Hierarchical Context Tagging for Utterance Rewriting](https://mlsanity.com/paper/2206.11218/) (2022)
- [The Elephant in the Coreference Room: Resolving Coreference in Full-Length French Fiction Works](https://mlsanity.com/paper/2510.15594/) (2025)
- [Stay Together: A System for Single and Split-antecedent Anaphora Resolution](https://mlsanity.com/paper/2104.05320/) (2021)
- [End-to-end Neural Information Status Classification](https://mlsanity.com/paper/2109.02753/) (2021)
- [Anatomy of OntoGUM--Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms](https://mlsanity.com/paper/2110.05727/) (2021)
- [Situation and Behavior Understanding by Trope Detection on Films](https://mlsanity.com/paper/2101.07632/) (2021)
- [Online Coreference Resolution for Dialogue Processing: Improving Mention-Linking on Real-Time Conversations](https://mlsanity.com/paper/2205.10670/) (2022)
- [Action-Item-Driven Summarization of Long Meeting Transcripts](https://mlsanity.com/paper/2312.17581/) (2023)
- [A New Sentence Ordering Method Using BERT Pretrained Model](https://mlsanity.com/paper/2108.11994/) (2021)

## Abstract (arXiv)

A long-standing challenge in coreference resolution has been the incorporation of entity-level information - features defined over clusters of mentions instead of mention pairs. We present a neural network based coreference system that produces high-dimensional vector representations for pairs of coreference clusters. Using these representations, our system learns when combining clusters is desirable. We train the system with a learning-to-search algorithm that teaches it which local decisions (cluster merges) will lead to a high-scoring final coreference partition. The system substantially outperforms the current state-of-the-art on the English and Chinese portions of the CoNLL 2012 Shared Task dataset despite using few hand-engineered features.