推荐学习书目
› Learn Python the Hard Way
Python Sites
› PyPI - Python Package Index
› http://diveintopython.org/toc/index.html
› Pocoo
值得关注的项目
› PyPy
› Celery
› Jinja2
› Read the Docs
› gevent
› pyenv
› virtualenv
› Stackless Python
› Beautiful Soup
› 结巴中文分词
› Green Unicorn
› Sentry
› Shovel
› Pyflakes
› pytest
Python 编程
› pep8 Checker
Styles
› PEP 8
› Google Python Style Guide
› Code Style from The Hitchhiker's Guide
这是一个创建于 2702 天前的主题，其中的信息可能已经有所发展或是发生改变。
DrQA 是一个阅读理解系统应用于开放领域的问答。
项目由 https://github.com/facebookresearch 发布。
项目地址： https://github.com/facebookresearch/DrQA
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。
我们实验 DrQA 专注于回答事实类问题，同时使用维基百科作为惟一的知识来源。维基百科是一个结构良好的大量，丰富，详细的文本来源。为了问答所有的问题，首先要接收一些潜在的相关文章，从 5 百万篇文章中，然后仔细扫描这些文本来找到答案。
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 questions 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： http://www.tensorflownews.com/
项目由 https://github.com/facebookresearch 发布。
项目地址： https://github.com/facebookresearch/DrQA
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。
我们实验 DrQA 专注于回答事实类问题，同时使用维基百科作为惟一的知识来源。维基百科是一个结构良好的大量，丰富，详细的文本来源。为了问答所有的问题，首先要接收一些潜在的相关文章，从 5 百万篇文章中，然后仔细扫描这些文本来找到答案。
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 questions 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： http://www.tensorflownews.com/
目前尚无回复
Select Language