Natural language processing

Natural language processing (NLP)

Classical NLP

The Classical NLP consists of creating a pipeline using processors to create annotations from text files.
Below is an example of a few processors.

• Tokenization
  • Convert a paragraph of test or a file into an array of words.
• Part-of-speech annotation
• Named Entity Recognition

Machine Learning

Datasets and Challenges

SQuAD

Link
The Stanford Question Answering Dataset. There are two versions of this dataset, 1.1 and 2.0.

Transformer

Main article: Transformer (machine learning model)

Attention is all you need paper

A neural network architecture by Google which uses encoder-decoder attention and self-attention.
It is currently the best at NLP tasks and has mostly replaced RNNs for these tasks.
However, it's computational complexity is quadratic in the number of input and output tokens due to attention.

Guides and explanations

• The Annotated Transformer
• Youtube Video

Google Bert

Main article: BERT (language model)

Github Link Paper Blog Post
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
A pretrained NLP neural network. Note the code is written in TensorFlow 1.

Albert

Github

A Lite BERT for Self-supervised Learning of Language Representations

This is a parameter reduction on Bert.

Libraries

Apache OpenNLP

Link