- SNLP01：绪论
- SNLP02：预备知识
- SNLP03：形式语言与自动机
- SNLP04：语料库与语言知识库
- SNLP05：语言模型
- SNLP06：概率图模型
- SNLP07：自动分词、命名实体识别与词性标注
- SNLP08：句法分析、语义分析、文本分类与情感分类概述
- Lecture01：Introduction and Word Vectors
- Lecture02：Word Vectors and Word Senses
- Lecture03：Neural Networks
- Lecture04：Backpropagation
- Lecture05：Dependency Parsing
- Lecture06：Language Models and RNNs
- Lecture07：Vanishing Gradients, Fancy RNNs
- Lecture08：Translation, Seq2Seq, Attention
- Lecture09：Practical Tips for Projects
- Lecture10：Question Answering
- Lecture11：Convolutional Networks for NLP
- Lecture12：Subword Models
- Lecture13：Contextual Word Embeddings
- Lecture14：Transformers and Self-Attention
- Lecture15：Natural Language Generation
- Lecture16：Coreference Resolution
- Lecture17：Multitask Learning
- Lecture18：Constituency Parsing, TreeRNNs
- Lecture19：Bias in AI
- Lecture20：Future of NLP + Deep Learning
- A Primer on Neural Network Models for Natural Language Processing
- Attention Is All You Need
- BERT Pre-training of Deep Bidirectional Transformers for Language Understanding
- Deep contextualized word representations
- Distributed Representations of Words and Phrases and their Compositionality
- Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
- Neural Architectures for Named Entity Recognition