{"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/word-embeddings-for-the-construction-domain","title":"Word Embeddings for the Construction Domain","arxiv_id":"1610.09333","date":"2016-10-28","proceeding":null,"authors":["Antoine J. -P. Tixier","Michalis Vazirgiannis","Matthew R. Hallowell"],"abstract":"We introduce word vectors for the construction domain. Our vectors were\nobtained by running word2vec on an 11M-word corpus that we created from scratch\nby leveraging freely-accessible online sources of construction-related text. We\nfirst explore the embedding space and show that our vectors capture meaningful\nconstruction-specific concepts. We then evaluate the performance of our vectors\nagainst that of ones trained on a 100B-word corpus (Google News) within the\nframework of an injury report classification task. Without any parameter\ntuning, our embeddings give competitive results, and outperform the Google News\nvectors in many cases. Using a keyword-based compression of the reports also\nleads to a significant speed-up with only a limited loss in performance. We\nrelease our corpus and the data set we created for the classification task as\npublicly available, in the hope that they will be used by future studies for\nbenchmarking and building on our work.","url_abs":"http://arxiv.org/abs/1610.09333v1","url_pdf":"http://arxiv.org/pdf/1610.09333v1.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":"word-embeddings-for-the-construction-domain","repo_url":"https://github.com/Tixierae/WECD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}