blocks|key|3837085|text|你可以阅读尤拉夫斯基和马丁的Speech+and+Language+Processing+(2008+edition)，这是该领域的标准教科书。这本书很长，而且有各种各样的主题，所以我建议只阅读真正适用于你兴趣的章节。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3837086|此外，最好的学习方法几乎肯定是从头开始实际实现NLP算法。您可以选择一些标准任务(语言建模、文本分类、词性标注、NER、解析)，并从头开始实现各种算法(ngram模型、HMM、朴素贝叶斯、MaxEnt、CKY)，以真正了解它们的工作原理。找到一些免费的数据集来测试您的实现也不会太难。|3837087|最后，有很多优秀的特定NLP算法的教程。例如，如果你想构建一个隐马尔可夫模型，我建议使用Jason+Eisner's+tutorial，它也涵盖了EM的平滑和无监督训练。如果你想为无监督的朴素贝叶斯训练实现吉布斯采样，我建议使用Philip+Resnik's+tutorial。|3837088|entityMap|0|LINK|mutability|MUTABLE|url|https://rads.stackoverflow.com/amzn/click/com/0131873210|1|http://www.cs.jhu.edu/~jason/465/hw-hmm/hw-hmm.pdf|2|http://www.umiacs.umd.edu/~resnik/pubs/gibbs.pdf^0|E|19|0|0|0|18|N|1|36|O|2|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|D|3|E|5|6|7|X|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|Y|8|@]|9|@$A|Z|B|10|1|11]|$A|12|B|13|1|14]]|C|$]]|$1|H|3|-4|5|6|7|15|8|@]|9|@]|C|$]]]|I|$J|$5|K|L|M|C|$N|O]]|P|$5|K|L|M|C|$N|Q]]|R|$5|K|L|M|C|$N|S]]]]

You could read Jurafsky and Martin's <a href="https://rads.stackoverflow.com/amzn/click/com/0131873210" rel="noreferrer" rel="nofollow noreferrer">Speech and Language Processing (2008 edition)</a>, which is the standard textbook in the field. It's long, and has a variety of topics, so I'd suggest reading just the chapters that really apply to your interests.

Further, the best way to learn is almost certainly to actually implement NLP algorithms from scratch. You could pick some standard tasks (language modeling, text classification, POS-tagging, NER, parsing) and implement various algorithms from the ground up (ngram models, HMMs, Naive Bayes, MaxEnt, CKY) to really understand what makes them work. It also shouldn't be too hard to find some free dataset to test your implementations on.

Finally, there are lots of tutorials out there for specific NLP algorithms that are excellent. For example, if you want to build an HMM, I suggest <a href="http://www.cs.jhu.edu/~jason/465/hw-hmm/hw-hmm.pdf" rel="noreferrer">Jason Eisner's tutorial</a> which also covers smoothing and unsupervised training with EM. If you want to implement Gibbs sampling for unsupervised Naive Bayes training, I suggest <a href="http://www.umiacs.umd.edu/~resnik/pubs/gibbs.pdf" rel="noreferrer">Philip Resnik's tutorial</a>.

blocks|key|2201667|text|除了朱拉夫斯基和马丁的书，克里斯托弗·D·曼宁和Hinrich+Schütze的Foundations+of+Statistical+Natural+Language+Processing也被广泛使用。对于IR，Manning等人。还写了Introduction+to+Information+Retrieval，可以在他们的网站上在线阅读或下载。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2201668|entityMap|0|LINK|mutability|MUTABLE|url|http://nlp.stanford.edu/fsnlp/promo/|1|http://nlp.stanford.edu/IR-book/^0|14|1I|0|3B|11|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]]]]

Aside from Jurafsky and Martin's book, Christopher D. Manning and Hinrich Schütze's <a href="http://nlp.stanford.edu/fsnlp/promo/" rel="noreferrer">Foundations of Statistical Natural Language Processing</a> is also widely used. For IR, Manning et al. also wrote <a href="http://nlp.stanford.edu/IR-book/" rel="noreferrer">Introduction to Information Retrieval</a> which can be read or downloaded online at their site.

blocks|key|3837148|text|如果你想获得关于如何使用自然语言的实用知识，你应该开始实现它。我建议在Python中使用NLTK(Natural+Language+Proecessing+Toolkit)。在python中实现NLP很容易。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3837149|您可以参考此链接http://nltk.org/|offset|length|3837150|或者你可以在http://cst.dk/online/pos_tagger/uk/上在线试用|3837151|entityMap|0|LINK|mutability|MUTABLE|url|http://nltk.org/|1|http://cst.dk/online/pos_tagger/uk/^0|0|8|G|0|0|6|Z|1|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|S|8|@]|9|@$D|T|E|U|1|V]]|A|$]]|$1|F|3|G|5|6|7|W|8|@]|9|@$D|X|E|Y|1|Z]]|A|$]]|$1|H|3|-4|5|6|7|10|8|@]|9|@]|A|$]]]|I|$J|$5|K|L|M|A|$N|O]]|P|$5|K|L|M|A|$N|Q]]]]

If you want practical knowledge on how can you work on Natural language you should start implementing it.
I suggest to use NLTK(Natural Language Proecessing Toolkit) with Python. Its easy to implement NLP in python.

You can refer to this link
<a href="http://nltk.org/">http://nltk.org/</a>

Or you can try it online on
<a href="http://cst.dk/online/pos_tagger/uk/">http://cst.dk/online/pos_tagger/uk/</a>

blocks|key|3837205|text|与其读一本特定的书，不如深入到论文的海洋中去，这可能是一个好主意。例如，http://www.aclweb.org包含许多关于自然语言处理的主题。通过这些论文，您可以获得对更多论文的引用，其中一些论文是NLP某个分支的基础。因为它们是由不同的作者写的，所以你不太可能受到一个观点的太多影响。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3837206|entityMap|0|LINK|mutability|MUTABLE|url|http://www.aclweb.org/^0|10|L|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

Instead of reading a specific book, diving into the sea of papers might be an as good idea. <a href="http://www.aclweb.org" rel="nofollow">http://www.aclweb.org</a>, for example, contains many topics on NLP. Through those papers, you get references to more papers, some of which are the foundations of a certain branch of NLP. And because they were written by different authors, you are unlikely to be influenced too much by one point of view.

blocks|key|2201836|text|如果您是一名Java开发人员，在http://alias-i.com/lingpipe/demos/tutorial/read-me.html上有大量教程介绍如何使用LingPipe构建NLP系统的组件。我写了一些教程和下面的一本书。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2201837|有几本书更多是面向工业的：|2201838|1)+Richard+M+Reese的Java自然语言处理|2201839|本文涵盖了如何使用一系列开源工具包(包括LingPipe)执行一些常见任务。|2201840|2)自然语言处理与Java和LingPipe食谱平装，作者:+Breck+Baldwin，Krishna+Dayanidhi|2201841|这本书在“构建组件”的层面上是任务驱动的，涵盖了驱动大多数文本驱动的NLP系统的主要技术。它不包括翻译。它比第一本书更详细，比LingPipe教程的覆盖面更广，但有时没有教程那么详细。|2201842|Breck|2201843|entityMap|0|LINK|mutability|MUTABLE|url|http://alias-i.com/lingpipe/demos/tutorial/read-me.html^0|G|1J|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@$A|Y|B|Z|1|10]]|C|$]]|$1|D|3|E|5|6|7|11|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|12|8|@]|9|@]|C|$]]|$1|H|3|I|5|6|7|13|8|@]|9|@]|C|$]]|$1|J|3|K|5|6|7|14|8|@]|9|@]|C|$]]|$1|L|3|M|5|6|7|15|8|@]|9|@]|C|$]]|$1|N|3|O|5|6|7|16|8|@]|9|@]|C|$]]|$1|P|3|-4|5|6|7|17|8|@]|9|@]|C|$]]]|Q|$R|$5|S|T|U|C|$V|W]]]]

If you are a Java developer there is an extensive list of tutorials for how to build components of NLP systems using LingPipe at <a href="http://alias-i.com/lingpipe/demos/tutorial/read-me.html" rel="nofollow">http://alias-i.com/lingpipe/demos/tutorial/read-me.html</a>. Full disclosure I wrote some of those tutorials and one of the books below.

There are a few books that are more industrially oriented:

1) Natural Language Processing with Java by Richard M Reese

This covers how to do some common tasks with a range of open source toolkits (including LingPipe).

2) Natural Language Processing with Java and LingPipe Cookbook Paperback 
by Breck Baldwin, Krishna Dayanidhi 

This book is task driven at the level of "get the component built" and covers the major technologies driving most NLP systems that are text driven. It does not cover translation. It goes into more detail than the first book and has broader coverage than the LingPipe tutorials but is sometimes less detailed than the tutorials. 

Breck

blocks|key|3927411|text|有一个名为TeLeMaCo的教学和学习材料中心。您可以找到有关NLP的许多方面的资源，并且可以轻松地添加您在web上找到的更多材料。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3927412|entityMap|0|LINK|mutability|MUTABLE|url|https://fedora.clarin-d.uni-saarland.de/hub/^0|5|8|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

There is a hub for teaching and learning materials called <a href="https://fedora.clarin-d.uni-saarland.de/hub/" rel="nofollow">TeLeMaCo</a>. You can find resources for many aspects of NLP, and you can easily add more materials that you have found on the web.

I recently attended a class on <a href="https://class.coursera.org/nlp/lecture" rel="noreferrer">coursera</a> about "Natural Language Processing" and I learnt a lot about parsing, IR and other interesting aspects like Q&amp;A etc. though I grasped the concepts well but I did not actually get any practical knowledge of it. Can anyone suggest me good online tutorials or books for Natural Language Processing?

Thanks

Tutorials For Natural Language Processing

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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我最近在上参加了一个关于“自然语言处理”的课程，我学到了很多关于解析、信息检索和其他有趣的方面的知识，比如Q&A等等。虽然我很好地掌握了这些概念，但我实际上并没有获得任何实际的知识。有没有人能给我推荐一些关于自然语言处理的在线教程或书籍？谢谢

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