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Unlike previous approaches, which\nrelied on supervised systems or heuristics to predict these relations, we treat\nrelations as latent variables in our neural entity-linking model. We induce the\nrelations without any supervision while optimizing the entity-linking system in\nan end-to-end fashion. Our multi-relational model achieves the best reported\nscores on the standard benchmark (AIDA-CoNLL) and substantially outperforms its\nrelation-agnostic version. Its training also converges much faster, suggesting\nthat the injected structural bias helps to explain regularities in the training\ndata.","url_abs":"http://arxiv.org/abs/1804.10637v1","url_pdf":"http://arxiv.org/pdf/1804.10637v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"improving-entity-linking-by-modeling-latent","repo_url":"https://github.com/lephong/mulrel-nel","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"improving-entity-linking-by-modeling-latent","repo_url":"https://github.com/facebookresearch/GENRE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"entity-linking","task_name":"Entity Linking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.10637","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.10637"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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