问题描述
我最近参加了一个类上 coursera 有关自然语言处理，我学到了很多东西有关分析，IR和其他有趣的方面，像Q＆安培; A等。虽然我掌握的概念很好，但我实际上并没有得到任何的实用知识。任何人都可以建议我良好的网上教程或书籍，自然语言处理？
I recently attended a class on coursera about "Natural Language Processing" and I learnt a lot about parsing, IR and other interesting aspects like Q&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?
感谢
推荐答案
您可以阅读Jurafsky和马丁的语音和语言处理（ 2008年版），这是该领域的标准教科书。它的长，并有各种主题的，所以我建议你阅读这一点真的适用于你的利益的章节。
You could read Jurafsky and Martin's Speech and Language Processing (2008 edition), 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.
此外，最好的学习方法是几乎可以肯定，真正实现自然语言处理算法，从头开始。你可以挑选一些标准任务（语言模型，文本分类，POS标记，NER，解析），并实施从地上爬起来的各种算法（NGRAM模型，HMM模型，朴素贝叶斯，最大墒，CKY）要真正了解是什么让他们的工作。它应该也不会太难找一些免费的数据集来测试您的实现。
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.
最后，有很多的教程在那里为特定的自然语言处理算法都非常优秀。例如，如果你想建立一个HMM，我建议杰森·艾斯纳的教程其中还包括滤波和无监督的训练与EM。如果你想实现Gibbs抽样无监督朴素贝叶斯的训练，我建议菲利普·雷斯尼克的教程。
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 Jason Eisner's tutorial which also covers smoothing and unsupervised training with EM. If you want to implement Gibbs sampling for unsupervised Naive Bayes training, I suggest Philip Resnik's tutorial.
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