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您可能想查看我之前的回答 类似的问题。
除此之外,大多数较轻的 NER 系统在很大程度上取决于所使用的域。 例如,您会发现大量有关生物医学 NER 系统的工具和论文。 除了我之前的文章(如果你想做 NER,它已经包含了我的主要建议),这里还有一些你可能想要研究的工具:
附加说明:如果不对输入进行标记化,您将无法逃脱。 自然语言的标记化有点不简单,这就是为什么我建议您使用一个可以同时完成这两件事的工具箱。
You might want to have a look at one of my earlier answers to a similar problem.
Other than that, most lighter NER systems depend a lot on the domain used. You will find a whole lot of tools and papers about biomedical NER systems, for example. In addition to my previous post (which already contains my main recommendation if you want to do NER), here are some more tools you might want to look into:
One additional remark: you won't get away without tokenization on the input. Tokenization of natural language is slightly non-trivial, that's why I suggest you use a toolbox that does both for you.
顺便说一句,我最近遇到了 OpenCalais ,它似乎具有我一直在寻找的功能。
BTW, I recently ran across OpenCalais which seems to havethe functionality I was looking after.
您可能还想尝试 Alchemy API。 它类似于开放加来。
You might want to try Alchemy API as well. Its similar to Open Calais.
对于 NLP 语法,您可以查看 http://code.google.com/p/graph-expression/ 和 http://gate.ac.uk/
For NLP grammar you can check http://code.google.com/p/graph-expression/ and http://gate.ac.uk/