{"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/transparent-efficient-and-robust-word","title":"Transparent, Efficient, and Robust Word Embedding Access with WOMBAT","arxiv_id":"1807.00717","date":"2018-07-02","proceeding":"COLING 2018 8","authors":["Mark-Christoph Müller","Michael Strube"],"abstract":"We present WOMBAT, a Python tool which supports NLP practitioners in\naccessing word embeddings from code. WOMBAT addresses common research problems,\nincluding unified access, scaling, and robust and reproducible preprocessing.\nCode that uses WOMBAT for accessing word embeddings is not only cleaner, more\nreadable, and easier to reuse, but also much more efficient than code using\nstandard in-memory methods: a Python script using WOMBAT for evaluating seven\nlarge word embedding collections (8.7M embedding vectors in total) on a simple\nSemEval sentence similarity task involving 250 raw sentence pairs completes in\nunder ten seconds end-to-end on a standard notebook computer.","url_abs":"http://arxiv.org/abs/1807.00717v1","url_pdf":"http://arxiv.org/pdf/1807.00717v1.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":"transparent-efficient-and-robust-word","repo_url":"https://github.com/nlpAThits/WOMBAT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-similarity","task_name":"Sentence Similarity"},{"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}