Natural Language Processing (CS224N): Introduction

Lecture: Natural Language Processing (2017, Stanford)
Professor: Christopher Manning and Richard Socher
Link: https://www.youtube.com/watch?v=OQQ-W_63UgQ&list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6
Slides: https://github.com/maxim5/cs224n-winter-2017

Goal of NLP
For computers to process or "understand" natural language in order to perform tasks that are useful. e.g. Machine translation and QA.

Why is NLP hard?
Human language is not a formal language.
It depends on real world, common sense, and contextual knowledge.
The large vocabulary, symbolic encoding of words leads to a sparsity problem.

Traditional machine learning vs. Deep learning
Traditional approach: use human-designed representation.
Deep Learning: facts are stored in vectors.

NLP progression
1. Mostly solved
Spell checking
Keyword search
Finding synonyms
Spam detection
POS tagging
Named entity recognition (NER)

2. Making good progress (thanks to DL)
Sentimental analysis
Coreference resolution
Word sense disambiguation
Parsing
Machine translation
Information extraction
Speech recognition

3. Still really hard
Spoken dialog systems
Question Answering
Paraphrase
Summarization