{"action":"opened","issue":{"url":"https://api.github.com/repos/lovit/wordvector_inference/issues/3","repository_url":"https://api.github.com/repos/lovit/wordvector_inference","labels_url":"https://api.github.com/repos/lovit/wordvector_inference/issues/3/labels{/name}","comments_url":"https://api.github.com/repos/lovit/wordvector_inference/issues/3/comments","events_url":"https://api.github.com/repos/lovit/wordvector_inference/issues/3/events","html_url":"https://github.com/lovit/wordvector_inference/issues/3","id":380114557,"node_id":"MDU6SXNzdWUzODAxMTQ1NTc=","number":3,"title":"Eigenvalue Weighting","user":{"login":"lovit","id":3401574,"node_id":"MDQ6VXNlcjM0MDE1NzQ=","avatar_url":"https://avatars1.githubusercontent.com/u/3401574?v=4","gravatar_id":"","url":"https://api.github.com/users/lovit","html_url":"https://github.com/lovit","followers_url":"https://api.github.com/users/lovit/followers","following_url":"https://api.github.com/users/lovit/following{/other_user}","gists_url":"https://api.github.com/users/lovit/gists{/gist_id}","starred_url":"https://api.github.com/users/lovit/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/lovit/subscriptions","organizations_url":"https://api.github.com/users/lovit/orgs","repos_url":"https://api.github.com/users/lovit/repos","events_url":"https://api.github.com/users/lovit/events{/privacy}","received_events_url":"https://api.github.com/users/lovit/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-11-13T08:41:34Z","updated_at":"2018-11-13T08:41:34Z","closed_at":null,"author_association":"OWNER","body":"SVD 를 통하여 PMI matrix 인 X 를 분해하면 다음과 같은 행렬을 얻음\r\n\r\n    X = USV\r\n\r\n주로 단어 벡터 W 와 문맥 벡터 C 를 아래처럼 정의함\r\n\r\n    W = US, C = V\r\n\r\n그러나 위처럼 단어/문맥 벡터를 정의할 경우 W 는 orthonomal 이지 않지만, C 는 orthonomal 형태를 띔. 이를 조절하기 위하여 다음처럼 W, C 를 정의하기도 함 (^1)\r\n\r\n    W = US^(1/2), C = S^(1/2)V\r\n\r\n^1 Levy, O., Goldberg, Y., & Dagan, I. (2015). Improving distributional similarity with lessons learned from word embeddings. Transactions of the Association for Computational Linguistics, 3, 211-225."}}
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