Lecture 1. Introduction — различия между версиями

Версия 23:33, 22 августа 2015

Lecture 1. Introduction

Brief history of NLP

• 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.

Major tasks of NLP

• 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

NLP techniques

• 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

Main problems

• 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:((((

About this course

It covers the following topics:

• Tokenization
• POS tagging
• Key word and phrase extraction
• Parsing
• Synonyms detection
• Language sources
• Topic modeling
• Text visualisation

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