最好的交钥匙关系检测库？
什么是最好的交钥匙（即用型、工业强度）关系检测库？
我一直在玩NLTK，但得到的结果不是很令人满意。
- http://nltk.googlecode.com/svn/trunk/doc/book /ch07.html
- http://nltk.googlecode.com /svn/trunk/doc/howto/relextract.html
理想情况下，我想要一个库，它可以接受类似这样的句子：
“莎拉杀死了一只正在吃孩子的狼”
，并将其转换为表示某种含义的数据结构比如：
被杀（莎拉，狼）和吃（狼，孩子）
我知道这是大量研究的主题，而且这不是一件容易的事。也就是说，有人知道有一个相当强大的现成库用于检测关系吗？
What is the best turnkey (ready to use, industrial-strength) relation detection library?
I have been playing around with NLTK and the results I get are not very satisfactory.
- http://nltk.googlecode.com/svn/trunk/doc/book/ch07.html
- http://nltk.googlecode.com/svn/trunk/doc/howto/relextract.html
Ideally, I would like a library that can take sentences like:
"Sarah killed a wolf that was eating a child"
and turn it into a data structure that means something like:
killed(Sarah, wolf) AND eating(wolf,child)
I know that this is the subject of a large body of research and that it is not an easy task. That said, is anyone aware of a reasonably robust ready-to-use library for detecting relations?
如果你对这篇内容有疑问，欢迎到本站社区发帖提问 参与讨论，获取更多帮助，或者扫码二维码加入 Web 技术交流群。
绑定邮箱获取回复消息
由于您还没有绑定你的真实邮箱，如果其他用户或者作者回复了您的评论，将不能在第一时间通知您！
发布评论
评论（2）
更新：Extractiv 不再可用。
Extractiv 的 点播 REST 服务：
http://rest.extractiv.com/extractiv/?url=https://stackoverflow.com/questions/4732686/best-turnkey-relation-detection-library&output_format=html_viewer 将处理此问题页面，提取并显示您想要的两个语义三元组左下角“GENERIC”下。 （它会丢弃 html 查看器中页面的一些文本，但如果您使用 json 或 rdf 输出，则不会丢弃该文本）。
这是假设您愿意接受商业、工业强度的解决方案，但允许有限的免费使用。它是一项网络服务，但可以使用开源库来访问它，或者可以从购买="http://languagecomputer.com" rel="nofollow noreferrer">语言计算机公司。
Update: Extractiv is no longer available.
Extractiv's On-Demand REST service:
http://rest.extractiv.com/extractiv/?url=https://stackoverflow.com/questions/4732686/best-turnkey-relation-detection-library&output_format=html_viewer will process this page, extract and display the two semantic triples you desire in the bottom left corner under "GENERIC". (It throws away some of the text from the page in the html viewer, but this text is not thrown away if you utilize json or rdf output).
This is assuming you're open to a commercial, industrial strength solution, though limited free usage is allowed. It's a web service but open source libraries can be used to access it or could be purchased from Language Computer Corporation.
这些关系可以很容易地从依赖符号的输出中读取出来。例如，放入Stanford Parser online，你可以看到两者您的示例中的主谓宾三元组在类型化依赖项中折叠表示为：
These relations can be read fairly easily out of the output of dependency notations. For instance, put into the Stanford Parser online, you can see both of the two subject-verb-object triples in your example in the typed dependencies collapsed representation as: