Lecture 1. Introduction

Ekaterina Chernyak, Dmitry Ilvovsky

Содержание

January 7, 1954 — Georgetown experiment. Russian to English machine translation;
1957 — Noam Chomsky introduced "universal grammar";
since 1961 — Brown Corpus;
the late 1960's — ELIZA, a simulation of a psychotherapist;
1975 — Vector Space Model by Salton;
up to the early 1980's — rule based approaches;
after the early 1980's — machine learning, corpus linguistics;
1998 — Language Model by Ponte and Croft;
since 1999 — topic modeling (LSI, pLSI, LDA, etc);
1999 — "Foundations of Statistical Natural Language Processing" by Manning and Shuetze;
2009 — "Natural Language Processing with Python" by Bird, Klein, and Loper.

Machine Translation
Text classification
- Sentiment analysis
- Spam filtering
- Classification by topic or by genre
Text clustering
Named entity recognition
Question answering
Automatic summarization
Natural language generation
Speech recognition
Spell checking
User study design and evaluation

The level of characters:
- Word segmentation
- Sentence breaking
The level of words — morphology:
- Part of speech (POS) tagging
- Word sense disambiguation
The level of sentences — syntax:
- Parsing
The level of senses — semantics:
- Coreference resolution
- Discourse analysis
- Semantic role labeling
- Synonymy detection

Ambiguity
- Lexical ambiguity:
- Time flies like an arrow; fruit flies like a banana.
Syntactic ambiguity
- Police help dog bite victim.
- Wanted: a nurse for a baby about twenty years old.
Neologism: unfriend, retweet, instagram
Different spelling: NY, New York City, New-York
Non-standard language: HIIII, how are u? miss u SOOOO much:((((

It covers the following topics:

You can try to use Python and R for various tasks.