{"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/near-lossless-binarization-of-word-embeddings","title":"Near-lossless Binarization of Word Embeddings","arxiv_id":"1803.09065","date":"2018-03-24","proceeding":null,"authors":["Julien Tissier","Christophe Gravier","Amaury Habrard"],"abstract":"Word embeddings are commonly used as a starting point in many NLP models to\nachieve state-of-the-art performances. However, with a large vocabulary and\nmany dimensions, these floating-point representations are expensive both in\nterms of memory and calculations which makes them unsuitable for use on\nlow-resource devices. The method proposed in this paper transforms real-valued\nembeddings into binary embeddings while preserving semantic information,\nrequiring only 128 or 256 bits for each vector. This leads to a small memory\nfootprint and fast vector operations. The model is based on an autoencoder\narchitecture, which also allows to reconstruct original vectors from the binary\nones. Experimental results on semantic similarity, text classification and\nsentiment analysis tasks show that the binarization of word embeddings only\nleads to a loss of ~2% in accuracy while vector size is reduced by 97%.\nFurthermore, a top-k benchmark demonstrates that using these binary vectors is\n30 times faster than using real-valued vectors.","url_abs":"http://arxiv.org/abs/1803.09065v3","url_pdf":"http://arxiv.org/pdf/1803.09065v3.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":"near-lossless-binarization-of-word-embeddings","repo_url":"https://github.com/tca19/near-lossless-binarization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"binarization","task_name":"Binarization"},{"task_slug":"semantic-similarity","task_name":"Semantic Similarity"},{"task_slug":"semantic-textual-similarity","task_name":"Semantic Textual Similarity"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1803.09065","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}