{"action":"opened","issue":{"url":"https://api.github.com/repos/RaRe-Technologies/gensim/issues/2114","repository_url":"https://api.github.com/repos/RaRe-Technologies/gensim","labels_url":"https://api.github.com/repos/RaRe-Technologies/gensim/issues/2114/labels{/name}","comments_url":"https://api.github.com/repos/RaRe-Technologies/gensim/issues/2114/comments","events_url":"https://api.github.com/repos/RaRe-Technologies/gensim/issues/2114/events","html_url":"https://github.com/RaRe-Technologies/gensim/issues/2114","id":336835893,"node_id":"MDU6SXNzdWUzMzY4MzU4OTM=","number":2114,"title":"Consider adding ability to downweight words far from window center","user":{"login":"zkurtz","id":4934167,"node_id":"MDQ6VXNlcjQ5MzQxNjc=","avatar_url":"https://avatars1.githubusercontent.com/u/4934167?v=4","gravatar_id":"","url":"https://api.github.com/users/zkurtz","html_url":"https://github.com/zkurtz","followers_url":"https://api.github.com/users/zkurtz/followers","following_url":"https://api.github.com/users/zkurtz/following{/other_user}","gists_url":"https://api.github.com/users/zkurtz/gists{/gist_id}","starred_url":"https://api.github.com/users/zkurtz/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/zkurtz/subscriptions","organizations_url":"https://api.github.com/users/zkurtz/orgs","repos_url":"https://api.github.com/users/zkurtz/repos","events_url":"https://api.github.com/users/zkurtz/events{/privacy}","received_events_url":"https://api.github.com/users/zkurtz/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-06-29T01:51:47Z","updated_at":"2018-06-29T01:51:47Z","closed_at":null,"author_association":"NONE","body":"The `window` parameter in word2vec controls how far apart two words can be and still directly influence each other's resulting embedding. The current setup is that a given word is in another word's window or it's not, a binary outcome. By analogy to kernel regression, word2vec uses a uniform (or boxcar) kernel to predict target words from input words. So, again by analogy to kernel regression, could we allow the user to specify the kernel as something other than uniform?\r\n\r\nFor example, when computing the loss, I'd like to be able to assign a weight to each context word, something like w = exp(-beta*k), where k is how far the context word is from the target word, and beta is nonnegative.  So allowing the user to select beta would be a start. Alternatively they could directly provide their own function of k.\r\n\r\nI've googled a bit but have not found anything related to this proposal for word2vec, but it seems commonsense enough that surely someone has tried it?"}}
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