{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/capturing-semantic-similarity-for-entity","title":"Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks","arxiv_id":"1604.00734","date":"2016-04-04","proceeding":"NAACL 2016 6","authors":["Matthew Francis-Landau","Greg Durrett","Dan Klein"],"abstract":"A key challenge in entity linking is making effective use of contextual\ninformation to disambiguate mentions that might refer to different entities in\ndifferent contexts. We present a model that uses convolutional neural networks\nto capture semantic correspondence between a mention's context and a proposed\ntarget entity. These convolutional networks operate at multiple granularities\nto exploit various kinds of topic information, and their rich parameterization\ngives them the capacity to learn which n-grams characterize different topics.\nWe combine these networks with a sparse linear model to achieve\nstate-of-the-art performance on multiple entity linking datasets, outperforming\nthe prior systems of Durrett and Klein (2014) and Nguyen et al. (2014).","url_abs":"http://arxiv.org/abs/1604.00734v1","url_pdf":"http://arxiv.org/pdf/1604.00734v1.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":"capturing-semantic-similarity-for-entity","repo_url":"https://github.com/matthewfl/nlp-entity-convnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"semantic-similarity","task_name":"Semantic Similarity"},{"task_slug":"semantic-textual-similarity","task_name":"Semantic Textual Similarity"},{"task_slug":"semantic-correspondence","task_name":"Semantic correspondence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.00734","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}