--- title: "Major Entity Identification — Manikantan 2024" description: "Coreference resolution (CR) systems transfer poorly to new domains." authors: ["Kawshik Manikantan", "Shubham Toshniwal", "Makarand Tapaswi", "Vineet Gandhi"] year: 2024 venue: "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing" source: "https://aclanthology.org/2024.emnlp-main.652/" doi: "10.18653/v1/2024.emnlp-main.652" 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/papers/manikantan-2024-major-entity-identification-generalizable-alternative/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/manikantan-2024-major-entity-identification-generalizable-alternative/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2024](https://md.research.elamur.ai/free/papers/2024/page/3/) / Major Entity Identification: A Generalizable Alternative to Coreference Resolution # Major Entity Identification: A Generalizable Alternative to Coreference Resolution **Authors:** Kawshik Manikantan, [Shubham Toshniwal](https://md.research.elamur.ai/free/authors/shubham-toshniwal/), Makarand Tapaswi, Vineet Gandhi · **Year:** 2024 · **Venue:** Proceedings of the 2024 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/2024.emnlp-main.652/) · [PDF](https://aclanthology.org/2024.emnlp-main.652.pdf) · [DOI 10.18653/v1/2024.emnlp-main.652](https://doi.org/10.18653/v1/2024.emnlp-main.652) · [OpenAlex](https://openalex.org/W4404792998) **Citations:** 4 citations (openalex-snapshot · 2026-10-14) · [9](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Coreference resolution (CR) systems transfer poorly to new domains. A major cause is that benchmarks disagree about what counts as a mention. The authors avoid annotating more target-domain data and instead reframe the problem as Major Entity Identification (MEI). The user supplies a short designative phrase for each frequent entity in a document. The system must then find every mention of those entities and assign all other spans to a null class. They turn two literary CR benchmarks, LitBank and FantasyCoref, into MEI data by treating frequently mentioned entities as major. For supervised learning they build MEIRa, a Longformer-Large entity-ranking model. For LLMs they design a two-stage few-shot prompt that tags words first and recovers full spans afterwards. The gap between in-domain and out-of-domain performance is much smaller for MEI than for CR. Prompted GPT-4 is only slightly behind supervised models. Because MEI is a classification problem, it supports simpler, more interpretable metrics. It also fits applications that need the mentions of specific, user-chosen entities. ## Key points - Major entities dominate narratives. In FantasyCoref, 942 of 5,829 entities (about 16%) produce 35,938 of 56,968 mentions (about 63%). Average cluster size grows from 3.67 to 34.66 (LitBank) and from 9.77 to 38.15 (FantasyCoref) when moving from CR to MEI. - MEIRa has three components: - a mention proposal network that detects all mentions, not only those of major entities; - an MLP that scores each mention against an entity memory, using log-distance metadata; - a threshold τ = 0 below which mentions go to the null entity. - MEIRa comes in two variants: - MEIRa-Static keeps the entity memory fixed, which allows full parallelization. It runs about 25x faster than a comparable CR model. - MEIRa-Hybrid averages scores from the updated memory and the initial phrase representations. - The LLM pipeline runs end-to-end without oracle mentions, unlike Le and Ritter (2023). Word-level tagging uses entity IDs, followed by a span-retrieval prompt. Most GPT-4 errors fall into the Missing Major and Extra-Major categories. - Code is released at github.com/KawshikManikantan/MEI. *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/shelves/linguistics/) Topics: [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/) ## Cited by Cited by 9 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 8 not yet. - [GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction](https://md.research.elamur.ai/free/papers/lin-2025-gum-sage-novel-dataset-approach/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{manikantan2024major, title = {{Major Entity Identification: A Generalizable Alternative to Coreference Resolution}}, author = {Manikantan, Kawshik and Toshniwal, Shubham and Tapaswi, Makarand and Gandhi, Vineet}, booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing}, year = {2024}, doi = {10.18653/v1/2024.emnlp-main.652}, url = {https://doi.org/10.18653/v1/2024.emnlp-main.652} } ``` - [← previous Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing](https://md.research.elamur.ai/free/papers/xu-2024-magpie-alignment-data-synthesis-scratch/) - [next → MAmmoTH2: Scaling Instructions from the Web](https://md.research.elamur.ai/free/papers/yue-2024-mammoth2-scaling-instructions-web/) - [↑ back to papers of 2024](https://md.research.elamur.ai/free/papers/2024/page/3/) ## References 49 references, 28 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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