Prediction, NLP

• 1: Classification
• 2: Continuous
• 3: NLP
• 4: Time Series
• 5: Image
• 6: Event And Anomaly

Introductions and Guides to NLP

• Ultimate Guide to Understand & Implement Natural Language Processing
• Introduction to NLP at Hackernoon is for people who suck at math - in their own words
• NLP Tutorial
• Deep Learning for NLP with Pytorch

Packages

• Scikit-learn: Machine learning in Python
• Natural Language Toolkit (NLTK)
• Pattern - A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.\
• spaCy - Industrial strength NLP with Python and Cython.Text Summarization

Text Summarisation

• TextRank- bringing order into text by Mihalcea and Tarau is regarded as the first paper on text summarization. The code is available here

• Modelling compressions with Discourse constraints by Clarke and Zapata provides a discourse informed model for summarization and subtitle generation.

• Deep Recurrent Generative decoder model for abstractive text summarization by Li et al, 2017 uses a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder.

• A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification by Ma and Sun, 2017 uses a gated attention enocder-decoder for text summarization.

Text Classification

• Convolutional Neural Networks for sentence classfication by Kim Yoon is now regarded as the standard baseline for text classification architecture.
• Using a CNN for text classification in TensorFlow by Denny Britz uses the same dataset as Kim Yoon’s paper(mentioned above). The code implementation can be found here.
• Facebook’s fasttext is a library for text embeddings and text classification
• Brightmart’s repo has a list of all text classification models with their respective scores, trainings,explanations and their Python implementations.
• Character-level Convolutional Networks for Text Classification by Zhang et al uses CNN and compares them with the traditional text classification models. Its Lua implementation can be found here.