{"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/semantic-matching-of-documents-from","title":"Semantic Matching of Documents from Heterogeneous Collections: A Simple and Transparent Method for Practical Applications","arxiv_id":"1904.12550","date":"2019-04-29","proceeding":null,"authors":["Mark-Christoph Müller"],"abstract":"We present a very simple, unsupervised method for the pairwise matching of\ndocuments from heterogeneous collections. We demonstrate our method with the\nConcept-Project matching task, which is a binary classification task involving\npairs of documents from heterogeneous collections. Although our method only\nemploys standard resources without any domain- or task-specific modifications,\nit clearly outperforms the more complex system of the original authors. In\naddition, our method is transparent, because it provides explicit information\nabout how a similarity score was computed, and efficient, because it is based\non the aggregation of (pre-computable) word-level similarities.","url_abs":"http://arxiv.org/abs/1904.12550v1","url_pdf":"http://arxiv.org/pdf/1904.12550v1.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":"semantic-matching-of-documents-from","repo_url":"https://github.com/nlpAThits/TopNCosSimAvg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}