Natural-Language-Processing

Resources to learn NLP

Key concepts:

1. Syntaxt and Parsing
2. Language Modeling and Word Sense Disambiguation
3. Part of Speech Tagging and Information Extraction:

• corpora
• simple N-grams
• word prediction
• stochastic tagging

4. Parsing and generation:

• generative grammar
• context-free grammars
• parsing and generation with context-free grammars
• weights and probabilities

5. Compositional and lexical semantics:

• semantic relations
• WordNet
• word sense
• word sense disambiguation

6. Question Answering
7. Text Summarization
8. Collocations and Information Retrieval
9. Sentiment Analysis and Semantics
10. Machine Translation

Video:

1. Lecture Collection - Natural Language Processing with Deep Learning

Scripts:

1. Natural Language Script

Python libraries and other tools:

1. Natural Language Toolkit
2. Sentence parsing, Stanford CoreNLP
3. Beginner resources in NLP

Presentations:

1. Natural Language Processing - Lecture Slides from the Coursera
2. Deep Learning for Natural Language Processing

Open datasets:

1. Kaggle datasets
2. Github nlp-datasets

Books:

1. Handbook of Natural Language Processing

2. Jurafsky, Daniel and James Martin, Speech and Language Processing, Prentice-Hall, 2000

Other resources: The intersection of language and technology

1. Representing Language
2. Representing Meaning
3. Words, Lexicons and Ontologies
4. The Mining and Knowledge Acquisition
5. Structured Text and The Semantic Web
6. Citation, Reputation and PageRank
7. Machine Translation Revisited
8. Analysis, Tagging, Parsing and Generation
9. Statistical and Example-based Machine Translation
10. Transfer and Word Sense Disambiguation
11. Final review and conclusions