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[标题党] DrQA 阅读维基百科来回答开放问题 Reading Wikipedia to Answer Open-Domain

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发表于 2017-7-27 10:39:30 | 显示全部楼层 |阅读模式
DrQA 是一个阅读理解系统应用于开放领域的问答
DrQA 是一个阅读理解系统用在开放领域问答。特别的,DrQA 针对一个机器阅读任务。在这个列表里,我们为一个潜在非常大的预料库中搜索一个问题的答案。所以,这个系统必须结合文本检索和机器文本理解。

DrQA is a system for reading comprehension applied to open-domain question answering. In particular, DrQA is targeted at the task of “machine reading at scale” (MRS). In this setting, we are searching for an answer to a question in a potentially very large corpus of unstructured documents (that may not be redundant). Thus the system has to combine the challenges of document retrieval (finding the relevant documents) with that of machine comprehension of text (identifying the answers from those documents).
Our experiments with DrQA focus on answering factoid questi** while using Wikipedia as the unique knowledge source for documents. Wikipedia is a well-suited source of large-scale, rich, detailed information. In order to answer any question, one must first retrieve the few potentially relevant articles among more than 5 million, and then scan them carefully to identify the answer.
更多资源:http://www.tensorflownews.com/


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