LING 388: Computers and Language

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Presentation transcript:

LING 388: Computers and Language Lecture 19

nltk book: Language Processing and Python 2 A Closer Look at Python: Texts as Lists of Words: http://www.nltk.org/book/ch01.html Assuming sent1,..,sent9 from nltk.book import *

nltk book: Language Processing and Python sent2, sent3 + for concatenation

nltk book: Language Processing and Python .append() to the end of the list (mutates the list) .append() vs .extend() to the end of the list: from stackoverflow.com

nltk book: Language Processing and Python Indexing [<index>]: Slices [<index>:<index>]: (can omit either <index>, default value)

nltk book: Language Processing and Python We know indexing works on strings (as well as lists): Repetition (*), Concatenation (+): .join() .split()

nltk book: Language Processing and Python Understanding check: Answer: Last two words by alphabetic sorting…

nltk book: Language Processing and Python 3.1 Frequency Distributions methods: .plot() .most_common() .hapaxes()

nltk book: Language Processing and Python

nltk book: Language Processing and Python specifically relevant to Moby Dick; other reported words are generic "English plumbing"

nltk book: Language Processing and Python

nltk book: Language Processing and Python Extract long words (using list comprehension):

nltk book: Language Processing and Python text5: chat corpus Pick out all the words longer than 7 characters that occur more than 7 times (using list comprehension) and sort them:

nltk book: Language Processing and Python Classes: FreqDist vs. Text

nltk book: Language Processing and Python Word length distribution (3.4 Counting Other Things)

nltk book: Language Processing and Python fdistl1.plot() fdistl1.plot(cumulative=True)