从哪里开始:使用 Python 的自然语言处理和人工智能 [英] Where to start: Natural language processing and AI using Python
问题描述
我的目标是编写一个能够从人类语言查询中提取语气、个性和意图的程序(例如，我输入:你今天过得怎么样?AI 系统会回复类似:好的.你好吗?)
我知道这是一个重要问题，那么我应该开始熟悉哪些深度学习主题以及哪些 Python 模块最有用?我已经开始研究 NLTK.谢谢.
规范的 AI 书籍将是 Stuart Russell 和 Peter Norvig 的 人工智能 和随附的课程:https://www.udacity.com/course/intro-to-artificial-intelligence--cs271
机器学习 (ML)
机器学习 (ML) 理论的一个很好的起点是 Andrew Ng 的机器学习课程.
机器学习和内部工作的一个很好的实际应用是University of华盛顿的机器学习课程与 Emily Fox 和 Carlos Guestrin 一起
自然语言处理 (NLP)
对于 NLP，一本流行的介绍书是 Jurafsky 和 Martin 的语音和语言处理
关于 NLP 的更多统计视角，Manning 和 Schutze 的 Statistical NLP 书籍/课程
对于 NLP 更实用的方法，NLTK book 是一个很好的起点:>
为了全面了解多年来的 NLP 技术，ACL 选集保存了大多数发表的著名 NLP 论文.
另一个很好的资源是浏览 NLP/ML/AI 的会议记录，Joel Tetreaul 保留了相关会议及其提交日期的更新列表:http://www.cs.rochester.edu/~tetreaul/conferences.html
其他资源
网上有大量 AI、ML、NLP 资源，以及最近的神经网络.一个你可以问任何问题的好论坛(不像 Stackoverflow 可能有限制)是 https://www.reddit.com/r/MachineLearning/ 可能更合适的 stackexchange 是 datascience.stackexchange.com
<小时>很棒的列表
您可以通过谷歌搜索 github 上的 awesome 列表轻松找到 AI/ML/NLP 的资源，例如:
- https://www.google.com/search?q=真棒+github+机器+学习
- https://www.google.com/search?q=awesome+github+natural+language+processing
- https://www.google.com/search?q=awesome+github+neural+net
- https://www.google.com/search?q=真棒+github+人工+智能
- https://www.google.com/search?q=awesome+github+deep+learning
My goal is to write a program capable of extracting tone, personality, and intent from human language inquiries (e.g. I type: How are you doing today? And the AI system responds with something like: Fine. How are you?)
I'm aware this is a non-trivial problem, so what deep-learning topics should I start becoming familiar with and what Python modules are most useful? I've already started looking at NLTK. Thanks.
The canonical AI book would be Stuart Russell and Peter Norvig's Artifical Intelligence and the accompanying course: https://www.udacity.com/course/intro-to-artificial-intelligence--cs271
Machine Learning (ML)
A good starting point for theoretical for machine learning (ML) which will be useful for more popular Natural Language Processing (NLP) task would be Andrew Ng's machine learning course.
And a good practical application of machine learning and the inner workings would be University of Washington's ML course with Emily Fox and Carlos Guestrin
Natural Language Processing (NLP)
As for NLP, a popular introduction book would be Jurafsky and Martin's Speech and Language Processing
As for a more statistical perspective to NLP, Manning and Schutze's Statistical NLP book/course
For a more practical approach to NLP, the NLTK book is a good starting point:
For a good overview of NLP technologies across the years, the ACL anthology keeps an archive of most of the notable NLP papers published.
And another good resource would be to go through conference proceedings of NLP/ML/AI, Joel Tetreaul keeps an updated list of related conferences and their submission dates: http://www.cs.rochester.edu/~tetreaul/conferences.html
Other resources
There're tonnes of AI, ML, NLP resources online and more recently neural nets. A good forum that you can ask anything (unlike Stackoverflow that might be restrictive) is https://www.reddit.com/r/MachineLearning/ and possibly a more appropriate stackexchange would be datascience.stackexchange.com
Awesome Lists
You can easily find resources for AI/ML/NLP easily by Googling for awesome lists on github, e.g.:
- https://www.google.com/search?q=awesome+github+machine+learning
- https://www.google.com/search?q=awesome+github+natural+language+processing
- https://www.google.com/search?q=awesome+github+neural+net
- https://www.google.com/search?q=awesome+github+artificial+intelligence
- https://www.google.com/search?q=awesome+github+deep+learning
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