{"action":"closed","issue":{"url":"https://api.github.com/repos/PaddlePaddle/Paddle/issues/8597","repository_url":"https://api.github.com/repos/PaddlePaddle/Paddle","labels_url":"https://api.github.com/repos/PaddlePaddle/Paddle/issues/8597/labels{/name}","comments_url":"https://api.github.com/repos/PaddlePaddle/Paddle/issues/8597/comments","events_url":"https://api.github.com/repos/PaddlePaddle/Paddle/issues/8597/events","html_url":"https://github.com/PaddlePaddle/Paddle/issues/8597","id":300473231,"number":8597,"title":"文本语义化召回","user":{"login":"wangbin11","id":1139811,"avatar_url":"https://avatars1.githubusercontent.com/u/1139811?v=4","gravatar_id":"","url":"https://api.github.com/users/wangbin11","html_url":"https://github.com/wangbin11","followers_url":"https://api.github.com/users/wangbin11/followers","following_url":"https://api.github.com/users/wangbin11/following{/other_user}","gists_url":"https://api.github.com/users/wangbin11/gists{/gist_id}","starred_url":"https://api.github.com/users/wangbin11/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/wangbin11/subscriptions","organizations_url":"https://api.github.com/users/wangbin11/orgs","repos_url":"https://api.github.com/users/wangbin11/repos","events_url":"https://api.github.com/users/wangbin11/events{/privacy}","received_events_url":"https://api.github.com/users/wangbin11/received_events","type":"User","site_admin":false},"labels":[{"id":651040732,"url":"https://api.github.com/repos/PaddlePaddle/Paddle/labels/User","name":"User","color":"5319e7","default":false}],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2018-02-27T02:41:19Z","updated_at":"2018-03-26T03:19:31Z","closed_at":"2018-03-26T03:19:31Z","author_association":"NONE","body":"最近在尝试用paddlepaddle提供的[dssm模型](https://github.com/PaddlePaddle/models/tree/develop/dssm)对query和doc做向量语义化，但是发现通过query向量语义化召回的很多doc结果毫无关联。比如query是”广洲新塘新围工业区“，召回了”新叶汤圆“ 还有”兴 宁 路 / 钓 台 路 ( 路 口 )“这种结果，cos相似度倒是挺高的，0.96。求教， 为什么会出现这样的无关语义召回结果呀。\r\n模型和paddlepaddle提供的dssm模型库基本一样，用的是cnn+回归，正负样本各2000w，单字切分（切词也试了，效果没太大区别）。word embeding是256维度，模型输出（短文本）的是128维，训练了25个pass。"}}
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