Probability for linguists
The University of Chicago
This paper oers a gentle introduction to probability for linguists, assuming little or no background beyond what one learns in high school.
The most important poin
GENERATIVE AND DISCRIMINATIVE
NAIVE BAYES AND LOGISTIC REGRESSION
Copyright c 2005, 2010. Tom M. Mitchell. All rights reserved.
*DRAFT OF January 19, 2010*
*PLEASE DO NOT DISTRIBUTE WITHOUT AUTHORS
An Empirical Study of Smoothing Techniques for
Stanley F. Chen
Computer Science Group
An Empirical Study of Smoothing Techniques for Language
San Jos State University
Linguistics & Language Development
LING165, Introduction to Natural Language Processing,
Clark Hall 485
LING165 Lab #4: Content selection for single document summarization
Your task for this lab assignment is to identify the 20 most informative words in an article from
the New Yorker: /home/students/ling165/s14/lab4/newyorker.txt. Define a word as
LING165 Lab #2: POS tagging using a first-order hidden Markov model
Part-of-speech (POS) tagging is the problem of identifying the POS of words in a given sentence.
Here, we follow a generative approach to solve the problem. That is, we assu
LING165 Lab #3: Spelling correction
Write a spelling correction program that does the following:
Take lines of words (one word per line) as input.
For each word, do the following:
o Look up the Brown Dictionary to see if its there.
o If it is ther
Data Clustering: A Review
Michigan State University
Indian Institute of Science
The Ohio State University
Clustering is the unsupervised classification of patterns (observations, data items,
or feature vectors) into gro