{"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/context-is-everything-finding-meaning","title":"Contextual Salience for Fast and Accurate Sentence Vectors","arxiv_id":"1803.08493","date":"2018-03-22","proceeding":null,"authors":["Eric Zelikman","Richard Socher"],"abstract":"Unsupervised vector representations of sentences or documents are a major building block for many language tasks such as sentiment classification. However, current methods are uninterpretable and slow or require large training datasets. Recent word vector-based proposals implicitly assume that distances in a word embedding space are equally important, regardless of context. We introduce contextual salience (CoSal), a measure of word importance that uses the distribution of context vectors to normalize distances and weights. CoSal relies on the insight that unusual word vectors disproportionately affect phrase vectors. A bag-of-words model with CoSal-based weights produces accurate unsupervised sentence or document representations for classification, requiring little computation to evaluate and only a single covariance calculation to ``train.\" CoSal supports small contexts, out-of context words and outperforms SkipThought on most benchmarks, beats tf-idf on all benchmarks, and is competitive with the unsupervised state-of-the-art.","url_abs":"https://arxiv.org/abs/1803.08493v6","url_pdf":"https://arxiv.org/pdf/1803.08493v6.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":"context-is-everything-finding-meaning","repo_url":"https://github.com/ezelikman/Context-Is-Everything","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"document-summarization","task_name":"Document Summarization"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-embeddings","task_name":"Sentence Embeddings"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment 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}