Natural Language Understanding with Distributed Representation

TL;DR

This lecture note introduces neural network-based methods for natural language understanding, focusing on language modeling and machine translation, providing foundational knowledge and practical insights for students and researchers.

Contribution

It offers a comprehensive, self-contained overview of neural network approaches to NLP, emphasizing language modeling and translation, suitable for educational purposes.

Findings

Neural networks are effective for NLP tasks.

Focus on language modeling and translation.

Provides foundational understanding for learners.

Abstract

This is a lecture note for the course DS-GA 3001 <Natural Language Understanding with Distributed Representation> at the Center for Data Science , New York University in Fall, 2015. As the name of the course suggests, this lecture note introduces readers to a neural network based approach to natural language understanding/processing. In order to make it as self-contained as possible, I spend much time on describing basics of machine learning and neural networks, only after which how they are used for natural languages is introduced. On the language front, I almost solely focus on language modelling and machine translation, two of which I personally find most fascinating and most fundamental to natural language understanding.