A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.

BERT for Multitask Learning

TensorFlow code and pre-trained models for A Dynamic Word Representation Model Based on Deep Context. It combines the idea of BERT model and ELMo's deep context word representation.

🔎 📄 SpExtor: Sparse Entity Extractor

This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date …

Bi-directional LSTM with attention for question answering

A C++ standalone library for machine learning

Python interface to CoreNLP using a bidirectional server-client interface.

An open-source NLP research library, built on PyTorch.

Text Simplification System and Dataset

Parse the dblp data into a structured format for experimentation.

Machine learning models to automatically summarise scientific papers

Science Parse parses scientific papers (in PDF form) and returns them in structured form.

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

Deep Transfer Learning Papers

Code for "Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization"

Convolutional Neural Network for Text Classification in Tensorflow

Neural Attention Model for Abstractive Summarization

Regex like pattern tree matching but on sentence's tree instead of Strings

code assigenments for Stanford NLP class.

Sent2Vec encoder and training code from the paper "Skip-Thought Vectors"

A python wrapper for the Stanford CoreNLP java library.

CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.

General purpose unsupervised sentence representations

A python tool for evaluating the quality of sentence embeddings.

Scientific Document Summarization Corpus and Annotations from the WING NUS group.

Oxford Deep NLP 2017 course

Useful research papers to help understand knowledge graph