Talk:Binary Independence Model
This article was nominated for deletion on 5 January 2019. The result of the discussion was Procedural close. |
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i was trying to make a resumed presentation of the model in question with source from that books pages. If it is still too close to the book's wording what should i do? would it be acceptable to refer people to the book link with the definition of the method and delete the definitions section? i am not sure i can rewrite better in my own words... thanks —Preceding unsigned comment added by Riclas (talk • contribs) 06:59, 28 January 2010
- I have done some rewriting. I think, given the technical and factual nature of the content this is now far enough from the source not to infringe copyright (and it is only a page or so from an entire book, although that is spread over many web pages, so more eligible for fair use). However I will leave the decision for another admin to review. DES (talk) 16:35, 28 January 2010 (UTC)
- Any reviewing admin should note that not all of the content on the page is in any way derived from the source, so this probbly shouldn't be a speedy in any case. DES (talk) 16:37, 28 January 2010 (UTC)
Close paraphrasing is still plagiarism and is still a copyright violation.
editThis article (definition section) was originally a blatant copy/paste of a copyrighted book. The subsequent edits changed it from a copy-paste to close paraphrase, but it is still plagiarizing the original content, and this still is a copyright violation. — Preceding unsigned comment added by Gaspanic99 (talk • contribs) 03:56, 7 January 2019 (UTC)
Yeah, it's quite bad. The article says:
"[...] where P ( x | R=1, q ) and P ( x | R=0, q ) are the probabilities of retrieving a relevant or nonrelevant document, respectively. If so, then that document's representation is x."
From "An Introduction to Information Retrieval" by Manning et al:
Here, P( x | R=1, q) and P( x | R=0, q) are the probability that if a relevant or nonrelevant, respectively, document is retrieved, then that document’s representation is x.
Source: https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf, Page 223 (or 260 if you're using PDF pages) — Preceding unsigned comment added by 2A02:2455:5A1:4700:B910:FB56:AE1F:7088 (talk) 15:53, 10 September 2020 (UTC)