{"action":"created","issue":{"url":"https://api.github.com/repos/hankcs/HanLP/issues/267","repository_url":"https://api.github.com/repos/hankcs/HanLP","labels_url":"https://api.github.com/repos/hankcs/HanLP/issues/267/labels{/name}","comments_url":"https://api.github.com/repos/hankcs/HanLP/issues/267/comments","events_url":"https://api.github.com/repos/hankcs/HanLP/issues/267/events","html_url":"https://github.com/hankcs/HanLP/issues/267","id":163787199,"number":267,"title":"地址集体识别","user":{"login":"llssyy","id":20194150,"avatar_url":"https://avatars.githubusercontent.com/u/20194150?v=3","gravatar_id":"","url":"https://api.github.com/users/llssyy","html_url":"https://github.com/llssyy","followers_url":"https://api.github.com/users/llssyy/followers","following_url":"https://api.github.com/users/llssyy/following{/other_user}","gists_url":"https://api.github.com/users/llssyy/gists{/gist_id}","starred_url":"https://api.github.com/users/llssyy/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/llssyy/subscriptions","organizations_url":"https://api.github.com/users/llssyy/orgs","repos_url":"https://api.github.com/users/llssyy/repos","events_url":"https://api.github.com/users/llssyy/events{/privacy}","received_events_url":"https://api.github.com/users/llssyy/received_events","type":"User","site_admin":false},"labels":[{"url":"https://api.github.com/repos/hankcs/HanLP/labels/question","name":"question","color":"cc317c"}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2016-07-05T07:26:25Z","updated_at":"2016-07-25T02:07:06Z","closed_at":null,"body":"您好，我是第一次使用hanlp，我想请教一下对于“北京市海淀区学院路31号北京航空航天大学体育馆”这个整体地址有什么方法可以集体一次性识别出来吗？而不是北京市/ns海淀区/ns学院路/nz31/m号/q北京航空航天大学/nt体育馆/n这中怎么把他们变为北京市海淀区学院路31号北京航空航天大学体育馆/ns。麻烦您了\r\n谢谢您。"},"comment":{"url":"https://api.github.com/repos/hankcs/HanLP/issues/comments/234821052","html_url":"https://github.com/hankcs/HanLP/issues/267#issuecomment-234821052","issue_url":"https://api.github.com/repos/hankcs/HanLP/issues/267","id":234821052,"user":{"login":"dianhu","id":834043,"avatar_url":"https://avatars.githubusercontent.com/u/834043?v=3","gravatar_id":"","url":"https://api.github.com/users/dianhu","html_url":"https://github.com/dianhu","followers_url":"https://api.github.com/users/dianhu/followers","following_url":"https://api.github.com/users/dianhu/following{/other_user}","gists_url":"https://api.github.com/users/dianhu/gists{/gist_id}","starred_url":"https://api.github.com/users/dianhu/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/dianhu/subscriptions","organizations_url":"https://api.github.com/users/dianhu/orgs","repos_url":"https://api.github.com/users/dianhu/repos","events_url":"https://api.github.com/users/dianhu/events{/privacy}","received_events_url":"https://api.github.com/users/dianhu/received_events","type":"User","site_admin":false},"created_at":"2016-07-25T02:07:06Z","updated_at":"2016-07-25T02:07:06Z","body":"@hankcs博主，你好：\r\n　目前我也在做一个地图的产品，其中要做一个自己的地址库。主要目的有二，一是对查询地址地址通过地址分词来返回词典中记录的相关地址的经纬度，二是当自己库中没有的时候，查询第三方的解析，然后通过分词(还有距离等因素)来判断第三方的解析哪个更靠谱。现在的做法是建了一个地址的词典，用的正向最大匹配，然后再根据地址词的上级下关系，上下文关系等一系列人定的规则来进行分词。遇到未登陆词，如分词结果不好，又人为的添加相应的规则。一般的地址能够很好的区分，如\"四川省成都市锦江区大慈市路远洋太古里\"分为\"四川省|2|成都市|3|锦江区|4|大慈市路|6|远洋太古里|10|\",但”贵州省安顺市东门坡东方时代广场“就分成了”贵州省|2|安顺市|3|东门|30|坡东方|4|时代广场|10|“，理想的是”贵州省|2|安顺市|3|东门坡|6|东方时代广场|10|“,当然这个和词典有很大关系。想请教一下，在词典只有相对少的情况下，如15万左右，有没有更好的基于统计的模型来解决地址方面的相关分词呢？"}}
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