Natural Language Understanding with Distributed Representation
arXiv:1511.07916
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.
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Cited by in corpus (16)
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- Search Engine Guided Non-Parametric Neural Machine Translation
- First Result on Arabic Neural Machine Translation
- MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification
- Deep Multitask Learning for Semantic Dependency Parsing
- Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition
- A GRU-based Encoder-Decoder Approach with Attention for Online Handwritten Mathematical Expression Recognition
- DenseRAN for Offline Handwritten Chinese Character Recognition
- Translating Phrases in Neural Machine Translation
- Keystroke dynamics as signal for shallow syntactic parsing
- Stroke Constrained Attention Network for Online Handwritten Mathematical Expression Recognition
- Trajectory-based Radical Analysis Network for Online Handwritten Chinese Character Recognition
- Radical analysis network for zero-shot learning in printed Chinese character recognition
- Getting Started with Neural Models for Semantic Matching in Web Search
- Textual Description for Mathematical Equations