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Free Resources and Tools to Help You Learn Natural Language Processing
- Speech and Language Processing by Jurafsky and Martin is the popularly acclaimed bible for traditional Natural Language Processing. You can access it here.
- For a more practical approach, you can try out Natural Language Toolkit.
- CS 224n: This is the best course to get started with using Deep Learning for Natural Language Processing. This course is hosted by Stanford and can be accessed here.
- Yoav Golberg's free and paid books are great resources to get started with Deep Learning in Natural Language Processing. The free version can be accessed here and the full version is available here.
- A very thorough coverage of all algorithms can be found in Jacob Einsenstein's notes from GATECH's NLP class which deals in almost all NLP methods. You can access the notes on GitHub here.
- Quick Getting Started guides / Knowing about what is hot and new
- Problem-Specific Surveys of Methods
- Blogs to follow regularly
- A survey paper by Young et al tries to summarize everything hip in Deep Learning based Natural Language Processing, and is recommended to get started with Natural Language Processing for practitioners. You can access the paper here.
- You can refer to this article to understand the basics of LSTMs and RNNs, which are used in Natural Language Processing a lot. Another much more cited (and highly reputed) survey of LSTMs is here. This is an interesting paper to understand how the hidden states of RNNs work. It is an enjoyable read and can be accessed here. I always recommend the following two blog posts anyone who hasn't read them:
- http://colah.github.io/posts/2015-08-Understanding-LSTMs
- https://distill.pub/2016/augmented-rnns/
- Convolutional Neural Networks (Convents) can be used to make sense of Natural Language. You can visualize how Convents work in NLP by reading this paper here.
- How Convents and RNNs compare with each other has been highlighted in this paper by Bai et al.. All its pytorch (I have stopped or reduced, to a large extent, reading deep learning code not written in pytorch ) code is open sourced here and gives you a feel of Godzilla v/s King Kong or Ford Mustang vs Chevy Camaro(if you enjoy(ed) that type of thing). Who will win!
- https://arxiv.org/abs/1801.06146
- https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf.
- https://gadgets.ndtv.com/social-networking/news/facebook-shuts-ai-system-after-bots-create-own-language-1731309
- https://www.forbes.com/sites/tonybradley/2017/07/31/facebook-ai-creates-its-own-language-in-creepy-preview-of-our-potential-future/#1d1ca041292c
- This paper by Google tells you how to solve a problem end-to-end when you have a lot of money and data.
- Facebook's Convolutional NMT system(just because of its cool convolutional approach) and its code is released as a library here.
- https://marian-nmt.github.io/is a framework for fast translation in C++http://www.aclweb.org/anthology/P18-4020
- http://opennmt.net/enables everyone to train their NMT systems.
- SQuAD dataset is a question answering datasets which tests an algorithm's ability to read comprehensions and answer questions. Microsoft published a paper earlier this year claiming they have reached human-level accuracy for the task. The paper can be found here. Another important algorithm (which I feel is the coolest) is Allen AI's BIDAF and its improvements.
- Another important set of algorithms is Visual Question Answering which answers questions about images. Teney et al.'s paperfrom VQA 2017 challenge is an excellent resource to get started. You can also find its implementations on Github here.
- Extractive Question Answering on large documents (like how Google Highlights answer to your queries in first few results) in real life can be done using Transfer Learning (thus with few annotations) as shown in this ETH paper here. A very good paper criticizing the "understanding" of Question Answering algorithms is here. Must read if you are working in this field.
- Natural Language Inference over Interaction Space â?? It highlights a very clever approach for putting a DenseNet (Convolutional Neural Network on Sentence representations). The fact that this was the outcome of an internship project makes it even cooler! You can read the paper here.
- This research paper from Omar Levy's group shows that even simple algorithms can perform the task. This is because algorithms are still not learning "inference".
- BiMPM is a cool model to predict paraphrases and can be accessed here.
- We have a new work for Paraphrase detection too which applies Relation Networks on top of sentence representations and has been accepted at this year's AINL conference. You can read it here.
- Language Modelling(LM) - Language Modelling is the task of learning an unsupervised representation of a language. This is done by predicting the (n+1)th word of a sentence given the first N words. These models have two important real-world uses, autocomplete and acting as a base model for transfer learning for text classification as mentioned above. A detailed survey is here. If you are interested in learning how to autocomplete LSTMs in cellphones/search engines work based on search history, here is a cool paper you should read.
- Relation Extraction - Relation extraction is the task of extracting relations between entities present in a sentence. A given sentence "A is related as r to B", gives the triplet (A,r, B). A survey of the research work in the field is here. Here is a research paper that I found to be really interesting. It uses BIDAFs for Zero Shot Relation extraction (that is, it can recognize relations it was not even trained to recognize).
- Dialog Systems - With the onset of the chatbot revolution, Dialog systems are now the rage. Many people (including us) make dialog systems as a combination of models such as intent detection, keyword detection, question answering etc, while others try to model it end-to-end. A detailed survey of dialog system models by the team at JD.com is here. I would also like to mention Parl.ai, a framework by Facebook AI for the purpose.
- Text Summarization - Text Summarization is used to get a condensed text from a document (paragraph/news article etc.). There are two ways to do this: extractive and abstractive summarization. While extractive summarization gives out sentences from the article with the highest information content (and what has been available for decades), abstractive summarization aims to write a summary just like a human would. This demo from Einstein AI brought abstractive summarization into mainstream research. There is an extensive survey of techniques here.
- Natural Language Generation (NLG) - Natural Language Generation is the research where the computer aims to write as a human would. This could be stories, poetries, image captions etc. Out of these, current research has been able to do very well on image captions where LSTMs and attention mechanism combined has given outputs usable in real life. A survey of techniques is available here.