blocks|key|1135565|text|基本上，命名为实体识别的算法很多，但最著名的是条件随机场(CRF)算法。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1135566|适用于Java|1135567|Spacy|unordered-list-item|1135568|
|1135569|NLTK|1135570|
的|1135571|斯坦福CoreNLP|ordered-list-item|1135572|1135573|另一个最有效的方法是使用像递归神经网络(LSTM)这样的深度学习。|1135574|1135575|http://nlp.town/blog/ner-and-the-road-to-deep-learning/|offset|length|1135576|https://towardsdatascience.com/deep-learning-for-ner-1-public-datasets-and-annotation-+methods-8b1ad5e98caf还有很多其他文章和论文希望这能有所帮助:)|1135577|1135578|entityMap|0|LINK|mutability|MUTABLE|url|1|https://towardsdatascience.com/deep-learning-for-ner-1-public-datasets-and-annotation-^0|0|0|0|0|0|0|0|0|0|0|0|1J|0|0|0|2E|1|0|0^^$0|@$1|2|3|4|5|6|7|19|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1A|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|1B|8|@]|9|@]|A|$]]|$1|G|3|H|5|6|7|1C|8|@]|9|@]|A|$]]|$1|I|3|J|5|F|7|1D|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|1E|8|@]|9|@]|A|$]]|$1|M|3|N|5|O|7|1F|8|@]|9|@]|A|$]]|$1|P|3|-4|5|6|7|1G|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|1H|8|@]|9|@]|A|$]]|$1|S|3|-4|5|6|7|1I|8|@]|9|@]|A|$]]|$1|T|3|U|5|O|7|1J|8|@]|9|@$V|1K|W|1L|1|1M]]|A|$]]|$1|X|3|Y|5|O|7|1N|8|@]|9|@$V|1O|W|1P|1|1Q]]|A|$]]|$1|Z|3|-4|5|6|7|1R|8|@]|9|@]|A|$]]|$1|10|3|-4|5|6|7|1S|8|@]|9|@]|A|$]]]|11|$12|$5|13|14|15|A|$16|U]]|17|$5|13|14|15|A|$16|18]]]]

Basically, There are lot of algorithm Named Entity Recognition but the most famous are
Conditional Random Field(CRF) Algorithm is used. 

<ol>
<li>Stanford CoreNLP for Java</li>
<li>Spacy</li>
<li>NLTK</li>
</ol>

Another most effective one is using Deep Learning like Recurrent Neural Network (LSTM). 

<ol>
<li><a href="http://nlp.town/blog/ner-and-the-road-to-deep-learning/" rel="nofollow noreferrer">http://nlp.town/blog/ner-and-the-road-to-deep-learning/</a></li>
<li><a href="https://towardsdatascience.com/deep-learning-for-ner-1-public-datasets-and-annotation-" rel="nofollow noreferrer">https://towardsdatascience.com/deep-learning-for-ner-1-public-datasets-and-annotation-</a> 
 methods-8b1ad5e98caf 
there are lot other articles and paper out there 
Hope this will help :)</li>
</ol>

blocks|key|4920703|text|NER可以由不同的算法执行，从使用grep：http://labs.fc.ul.pt/mer/的简单字符串匹配到高级机器学习技术：https://nlp.stanford.edu/software/CRF-NER.shtml|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4920704|entityMap|0|LINK|mutability|MUTABLE|url|http://labs.fc.ul.pt/mer/|1|https://nlp.stanford.edu/software/CRF-NER.shtml^0|M|P|0|1T|1B|1|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]|$A|R|B|S|1|T]]|C|$]]|$1|D|3|-4|5|6|7|U|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]|L|$5|G|H|I|C|$J|M]]]]

NER can be performed by different algorithms, from simple string matching using grep: <a href="http://labs.fc.ul.pt/mer/" rel="nofollow noreferrer">http://labs.fc.ul.pt/mer/</a> to advanced machine learning techniques: <a href="https://nlp.stanford.edu/software/CRF-NER.shtml" rel="nofollow noreferrer">https://nlp.stanford.edu/software/CRF-NER.shtml</a>

blocks|key|3452104|text|基本上，这取决于您所遵循的方法。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3452105|对于名称实体识别，可以使用n+no.of方法。最典型的是使用条件随机场算法。另一个最有效的方法是使用深度学习，如递归神经网络。可能还有其他的算法。|offset|length|style|BOLD|3452106|希望这能有所帮助:)|3452107|entityMap^0|0|D|1|U|5|1K|6|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|M|8|@$D|N|E|O|F|G]|$D|P|E|Q|F|G]|$D|R|E|S|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|T|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|U|8|@]|9|@]|A|$]]]|K|$]]

Basically, It Depends on the approach that you are following.

For Name Entity Recognition There are n no.of approaches available. Most typically Conditional Random Field(CRF) Algorithm is used. Another most effective one is using Deep Learning like Recurrent Neural Network (LSTM). There might be other algorithms as well.

Hope this will help :)

blocks|key|1163676|text|如果你想在Keras中尝试一个很好的、预先构建的双向CRF-LSTM+(目前性能较好的模型之一)，我会推荐python包Anango作为使用NER系统的一种非常快捷的方式。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1163677|尽管这个最初的问题是用Java标记的。重要的是要指出，你将找到所有最新的和最好的算法，这类机器学习是用Python实现的。|1163678|Spacy+(一个Python包)也是一个非常好的入门方式，尽管我发现它需要更多的时间来快速设置。此外，我也不确定改变他们预先构建的NER算法有多容易。我相信它使用的是CNN。|1163679|很好地概述了NER的现状：A+Survey+on+Recent+Advances+in+Named+Entity+Recognition+from+Deep+Learning+models|1163680|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/Hironsan/anago|1|http://www.aclweb.org/anthology/C18-1182^0|1O|6|0|0|0|0|D|29|1|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|D|3|E|5|6|7|X|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|Y|8|@]|9|@]|C|$]]|$1|H|3|I|5|6|7|Z|8|@]|9|@$A|10|B|11|1|12]]|C|$]]|$1|J|3|-4|5|6|7|13|8|@]|9|@]|C|$]]]|K|$L|$5|M|N|O|C|$P|Q]]|R|$5|M|N|O|C|$P|S]]]]

If you want to play around with a nice, pre-built Bidirectional CRF-LSTM (currently one of the better performing models) in Keras, I would recommend the python package <a href="https://github.com/Hironsan/anago" rel="nofollow noreferrer">Anango</a> as a very quick way to play around with an NER system. 

Although this initial question is tagged with Java. It is important to point out that you are going to find all the latest and greatest algorithms for this type of Machine Learning implemented in Python.

Spacy (a Python package) is also a very good way to get started although I found it took a little more time to set something up quickly. Also, I'm not sure how easy it would be to alter their NER algorithm that is pre-built. I believe it uses CNNs.

A good overview of where things are at with NER:
<a href="http://www.aclweb.org/anthology/C18-1182" rel="nofollow noreferrer">A Survey on Recent Advances in Named Entity Recognition from Deep
Learning models</a>

What algorithm does Named Entity Recognition (NER) use? I mean how does it match and tag all the entities?

NLP - Named Entity Recognition

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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命名实体识别(NER)使用什么算法？我的意思是它如何匹配和标记所有的实体？

问NLP -命名实体识别
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