{"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/why-so-down-the-role-of-negative-and-positive","title":"Why So Down? The Role of Negative (and Positive) Pointwise Mutual Information in Distributional Semantics","arxiv_id":"1908.06941","date":"2019-08-19","proceeding":null,"authors":["Alexandre Salle","Aline Villavicencio"],"abstract":"In distributional semantics, the pointwise mutual information ($\\mathit{PMI}$) weighting of the cooccurrence matrix performs far better than raw counts. There is, however, an issue with unobserved pair cooccurrences as $\\mathit{PMI}$ goes to negative infinity. This problem is aggravated by unreliable statistics from finite corpora which lead to a large number of such pairs. A common practice is to clip negative $\\mathit{PMI}$ ($\\mathit{\\texttt{-} PMI}$) at $0$, also known as Positive $\\mathit{PMI}$ ($\\mathit{PPMI}$). In this paper, we investigate alternative ways of dealing with $\\mathit{\\texttt{-} PMI}$ and, more importantly, study the role that negative information plays in the performance of a low-rank, weighted factorization of different $\\mathit{PMI}$ matrices. Using various semantic and syntactic tasks as probes into models which use either negative or positive $\\mathit{PMI}$ (or both), we find that most of the encoded semantics and syntax come from positive $\\mathit{PMI}$, in contrast to $\\mathit{\\texttt{-} PMI}$ which contributes almost exclusively syntactic information. Our findings deepen our understanding of distributional semantics, while also introducing novel $PMI$ variants and grounding the popular $PPMI$ measure.","url_abs":"https://arxiv.org/abs/1908.06941v1","url_pdf":"https://arxiv.org/pdf/1908.06941v1.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":"why-so-down-the-role-of-negative-and-positive","repo_url":"https://github.com/alexandres/lexvec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1908.06941","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}