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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
- 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
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.
- 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.