Introduction to Natural Language Processing PowerPoint Presentation, PPT - DocSlides

Introduction to Natural Language Processing

Source: Natural Language Processing with Python --- Analyzing Text with the Natural Language Toolkit

Status

We have had three weeks of Object-Oriented Programming in Python

Simple I/O, File I/O

Lists, Strings,

Tuples

, and their methods

Numeric types and operations

Control structures: if, for, while

Function definition and use

Parameters for defining the function, arguments for calling the function

Applying what we have

The first chapter of the NLTK book repeats much of what we have seen

Now in the context of an application domain: Natural Language Processing

Note: there are similar packages for other domains

Book examples in chapter 1 are all done with the interactive python shell

Reasons

What can we achieve by combining simple programming techniques with large quantities of text?How can we automatically extract key words and phrases that sum up the style and content of a text?What tools and techniques does the Python programming language provide for such work?What are some of the interesting challenges of natural language processing?

Quote from nltk book

Since text can cover any subject area, it is a general interest area to explore in some depth.

The NLTK

The natural language tool kit modulesdatasetstutorialsContains: align, app (package), book, ccg (package), chat (package, chunk (package), classify (package), cluster (package), collocations, compat, containers, corpus (package), data, decorators, downloader, draw (package), etree (package), evaluate, examples (package), featstruct, grammar), help, inference (package), internals, lazyimport, metrics (package), misc (package), model (package), olac, parse (package), probability, sem (package), sourcedstring, stem (package), tag (package), text, tokenize (package), toolbox (package), tree, treetransforms, util, yamltags

We will not have time to explore all of them, but this gives a full list for further exploration.

NLTK functions

all(...

)

all

(iterable

) ->

bool

Return

True if

bool(x

) is True for all values

x

in the

iterable

.

any

(...)

any(iterable

) ->

bool

Return

True if

bool(x

) is True for any

x

in the

iterable

.

Using the NLTK

>>> import

nltk>>> nltk.download()

opens a window showing this:

Do it now

Getting data from the downloaded files

Previously, we used

from math import pi

to get something specific from a module

Now, from the

nltk.book

, we will get the text files we will use

from

nltk.book

import *

Import the data files

>>> import nltk>>> from nltk.book import **** Introductory Examples for the NLTK Book ***Loading text1, ..., text9 and sent1, ..., sent9Type the name of the text or sentence to view it.Type: 'texts()' or 'sents()' to list the materials.text1: Moby Dick by Herman Melville 1851text2: Sense and Sensibility by Jane Austen 1811text3: The Book of Genesistext4: Inaugural Address Corpustext5: Chat Corpustext6: Monty Python and the Holy Grailtext7: Wall Street Journaltext8: Personals Corpustext9: The Man Who Was Thursday by G . K . Chesterton 1908

Do it now.Then type sent1 at a python prompt to see the fist sentence of Moby DickRepeat for sent2 .. sent9 to see the first sentence of each text.Take note of the collection of texts. Great variety. Different ones will be useful for different types of exploration

What type of data is each first sentence?

Searching the texts

>>> text9.concordance("sunset")Building index...Displaying 14 of 14 matches:E suburb of Saffron Park lay on the sunset side of London , as red and ragged n , as red and ragged as a cloud of sunset . It was built of a bright brick thbered in that place for its strange sunset . It looked like the end of the worival ; it was upon the night of the sunset that his solitude suddenly ended . he Embankment once under a dark red sunset . The red river reflected the red sst seemed of fiercer flame than the sunset it mirrored . It looked like a strehe passionate plumage of the cloudy sunset had been swept away , and a naked mder the sea . The sealed and sullen sunset behind the dark dome of St . Paul 'ming with the colour and quality of sunset . The Colonel suggested that , befogold . Up this side street the last sunset light shone as sharp and narrow as of gas , which in the full flush of sunset seemed coloured like a sunset cloudsh of sunset seemed coloured like a sunset cloud . " After all ," he said , " y and quietly , like a long , low , sunset cloud , a long , low house , mellowhouse , mellow in the mild light of sunset . All the six friends compared note

A concordance shows a word in context

Same word in different texts

>>> text1.concordance("monstrous")Building index...Displaying 11 of 11 matches:ong the former , one was of a most monstrous size . ... This came towards us , ON OF THE PSALMS . " Touching that monstrous bulk of the whale or ork we have rll over with a heathenish array of monstrous clubs and spears . Some were thickd as you gazed , and wondered what monstrous cannibal and savage could ever havthat has survived the flood ; most monstrous and most mountainous ! That Himmalthey might scout at Moby Dick as a monstrous fable , or still worse and more deth of Radney .'" CHAPTER 55 Of the Monstrous Pictures of Whales . I shall ere ling Scenes . In connexion with the monstrous pictures of whales , I am stronglyere to enter upon those still more monstrous stories of them which are to be foght have been rummaged out of this monstrous cabinet there is no telling . But of Whale - Bones ; for Whales of a monstrous size are oftentimes cast up dead u>>> text2.concordance("monstrous")Building index...Displaying 11 of 11 matches:. " Now , Palmer , you shall see a monstrous pretty girl ." He immediately wentyour sister is to marry him . I am monstrous glad of it , for then I shall haveou may tell your sister . She is a monstrous lucky girl to get him , upon my hok how you will like them . Lucy is monstrous pretty , and so good humoured and Jennings , " I am sure I shall be monstrous glad of Miss Marianne ' s company usual noisy cheerfulness , " I am monstrous glad to see you -- sorry I could nt however , as it turns out , I am monstrous glad there was never any thing in so scornfully ! for they say he is monstrous fond of her , as well he may . I spossible that she should ." " I am monstrous glad of it . Good gracious ! I havthing of the kind . So then he was monstrous happy , and talked on some time abe very genteel people . He makes a monstrous deal of money , and they keep thei>>>

Moby Dick

Sense and Sensibility

>>> text1.similar("monstrous")

abundant candid careful christian contemptible curious delightfullydetermined doleful domineering exasperate fearless few gamesomehorrible impalpable imperial lamentable lazy loving>>>

>>> text2.similar("monstrous")Building word-context index...very exceedingly heartily so a amazingly as extremely good greatremarkably sweet vast>>>

Note different sense of the word in the two texts.

Spot check

Choose a word and generate a concordance for it in two or three texts.Do you see any difference in meaning?Look for similar terms in the texts.

Not sure what words are in what texts?

“<word>” in

text

n

will return true or false

Look at the first sentence to get some words that are in the text.

Guess. ex: “money” appears in all but text6 and text8

Looking at vocabulary

>>> len(set(text3))2789>>> len(set(text2))6833>>>

>>> len(text3)44764>>>

Total number of tokens, includes non words and repeated words

What do these numbers mean?

>>> float(len(text2))/float(len(set(text2)))

20.719449729255086>>>

What does this tell us?

On average, a word is used > 20 times

A rough measure of lexical richness

>>> from __future__ import division>>> 100*text2.count("money")/len(text2)0.018364694581002431>>>

Note two ways to get floating point results when dividing integers

What does this tell us?

Making life easier

>>> lexical_diversity(text2)20.719449729255086>>> percentage(text2.count('money'),len(text2))0.018364694581002431>>>

>>> def

lexical_diversity(text

):

... return

len(text

) /

len(set(text

))

...

>>> def

percentage(count,total

):

... return 100*count/total

...

Spot check

Modify the function percentage so that you only have to pass it the name of the text and the word to count

the new call will look like this:

percentage(text2, “money”)

In which of the texts is “money” most dominant?

Where is it least dominant?

What are the percentages for each text?

Indexing the texts

Each of the texts is a list, and so all our list methods work, including slicing:

>>> text2[0:100]['[', 'Sense', 'and', 'Sensibility', 'by', 'Jane', 'Austen', '1811', ']', 'CHAPTER', '1', 'The', 'family', 'of', 'Dashwood', 'had', 'long', 'been', 'settled', 'in', 'Sussex', '.', 'Their', 'estate', 'was', 'large', ',', 'and', 'their', 'residence', 'was', 'at', 'Norland', 'Park', ',', 'in', 'the', 'centre', 'of', 'their', 'property', ',', 'where', ',', 'for', 'many', 'generations', ',', 'they', 'had', 'lived', 'in', 'so', 'respectable', 'a', 'manner', 'as', 'to', 'engage', 'the', 'general', 'good', 'opinion', 'of', 'their', 'surrounding', 'acquaintance', '.', 'The', 'late', 'owner', 'of', 'this', 'estate', 'was', 'a', 'single', 'man', ',', 'who', 'lived', 'to', 'a', 'very', 'advanced', 'age', ',', 'and', 'who', 'for', 'many', 'years', 'of', 'his', 'life', ',', 'had', 'a', 'constant', 'companion']>>>

The first 101 elements in the list for text2 (Sense and Sensibility) Note that the first element is itself a list.

Text index

We can see what is at a position:>>> text2[302]'devolved’And where a word appears:>>> text2.index('marriage')255>>>

Remember that indexing begins at 0 and the index tells how far removed you are from the initial element.

Strings

Each of the elements in each of the text lists is a string, and all the string methods apply.

Frequency distributions

>>> fdist1=FreqDist(text1)>>> fdist1<FreqDist with 260819 outcomes>>>> vocabulary1=fdist1.keys()>>> vocabulary1[:50][',', 'the', '.', 'of', 'and', 'a', 'to', ';', 'in', 'that', "'", '-', 'his', 'it', 'I', 's', 'is', 'he', 'with', 'was', 'as', '"', 'all', 'for', 'this', '!', 'at', 'by', 'but', 'not', '--', 'him', 'from', 'be', 'on', 'so', 'whale', 'one', 'you', 'had', 'have', 'there', 'But', 'or', 'were', 'now', 'which', '?', 'me', 'like']>>>

These are the 50 most common tokens in the text of Moby Dick. Many of these are not useful in characterizing the text. We call them “stop words” and will see how to eliminate them from consideration later.

More precise specification

Consider the mathematical expressionPython implementation is[w for w in V if p(w)]

>>> AustenVoc=set(text2)>>> long_words_2=[w for w in AustenVoc if len(w) >15]>>> long_words_2['incomprehensible', 'disqualifications', 'disinterestedness', 'companionableness']>>>

List comprehension – we saw it first last week

Add to the condition

fdist2=FreqDist(text2)>>> long_words_2=sorted([w for w in AustenVoc if len(w) >12 and fdist2[w]>5])>>> long_words_2['Somersetshire', 'accommodation', 'circumstances', 'communication', 'consciousness', 'consideration', 'disappointment', 'distinguished', 'embarrassment', 'encouragement', 'establishment', 'extraordinary', 'inconvenience', 'indisposition', 'neighbourhood', 'unaccountable', 'uncomfortable', 'understanding', 'unfortunately']

So, our

if

p(w

) can be as complex as we need

Spot check

Find all the words longer than 12 characters, which occur at least 5 times, in each of the texts.

How well do they give you a sense of the texts?

Collocations and Bigrams

Sometimes a word by itself is not representative of its role in a text. It is only with a companion word that we get the intended sense.

red wine

high horse

sign of hope

Bigrams are two word combinations

not all bigrams are useful, of course

len(bigrams(text2)) == 141575

including “and among”, “they could” , …

Collocations provides bigrams that include uncommon words – words that might be significant in the text.

text2.collocations has 20 pairs

>>> colloc2=text2.collocations()Colonel Brandon; Sir John; Lady Middleton; Miss Dashwood; every thing;thousand pounds; dare say; Miss Steeles; said Elinor; Miss Steele;every body; John Dashwood; great deal; Harley Street; Berkeley Street;Miss Dashwoods; young man; Combe Magna; every day; next morning>>> [len(w) for w in text2][1, 5, 3, 11, 2, 4, 6, 4, 1, 7, 1, 3, 6, 2, 8, 3, 4, 4, 7, 2, 6, 1, 5, 6, 3, 5, 1, 3, 5, 9, 3, 2, 7, 4, 1, 2, 3, 6, 2, 5, 8, 1, 5, 1, 3, 4, 11, 1, 4, 3, 5, 2, 2, 11, 1, 6, 2, 2, 6, 3, 7, 4, 7, 2, 5, 11, 12, 1, 3, 4, 5, 2, 4, 6, 3, 1, 6, 3, 1, 3, 5, 2, 1, 4, 8, 3, 1, 3, 3, 3, 4, 5, 2, 3, 4, 1, 3, 1, 8, 9, 3, 11, 2, 3, 6, 1, 3, 3, 5, 1, 5, 8, 3, 5, 6, 3, 3, 1, 8, …

For each word in text2, return its length

>>> fdist2=FreqDist([len(w) for w in text2])>>> fdist2<FreqDist with 141576 outcomes>>>> fdist2.keys()[3, 2, 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 16]>>>

There are 141,576 words, each with a length. But there are only 17 different word lengths.

>>> fdist2.items()

[(3, 28839), (2, 24826), (1, 23009), (4, 21352), (5, 11438), (6, 9507), (7, 8158), (8, 5676), (9, 3736), (10, 2596), (11, 1278), (12, 711), (13, 334), (14, 87), (15, 24), (17, 3), (16, 2)]>>>

There are 28,839 3-letter words in Sense and Sensibility (not unique words, necessarily)

>>> fdist2.keys()[3, 2, 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 16]>>> fdist2.items()[(3, 28839), (2, 24826), (1, 23009), (4, 21352), (5, 11438), (6, 9507), (7, 8158), (8, 5676), (9, 3736), (10, 2596), (11, 1278), (12, 711), (13, 334), (14, 87), (15, 24), (17, 3), (16, 2)]>>> fdist2.max()3>>> fdist2[3]28839>>> fdist2[13]334>>>

There are 28,839 3-letter words and 334 13-letter words in Sense and Sensibility

Table 1.2 – FreqDist functions

Example

Descripiton

fdist

=

FreqDist(samples

)

create a frequency distribution containing the given samples

fdist.inc(sample

)

increment the count for this sample

fdist['monstrous

']

count of the number of times a given sample occurred

fdist.freq(‘monstrous

’)

frequency of a given sample

fdist.N

()

total number of samples

fdist.keys

()

The samples sorted in order of decreasing frequency

for sample in

fdist

:

iterate over the samples, in order of decreasing frequency

fdist.max

()

sample

with the greatest count

fdist.tabulate

()

tabulate the frequency distribution

fdist.plot

()

graphical plot of the frequency

distribution

fdist.plot(cumulative

=True)

cumulative plot of the frequency distribution

fdist1<fdist2

test if samples in fdist1

occur less frequently than in fdist2

Conditionals

Function Meanings.startswith(t) test if s starts with ts.endswith(t) test if s ends with tt in s test if t is contained inside ss.islower() test if all cased characters in s are lowercases.isupper() test if all cased characters in s are uppercases.isalpha() test if all characters in s are alphabetics.isalnum() test if all characters in s are alphanumerics.isdigit() test if all characters in s are digitss.istitle() test if s is titlecased (all words in s have have initial capitals)

We have seen conditionals and loop statements. These are some special functions for work on text

Spot check

>>> sorted([w for w in set(text7) if '-' in w and 'index' in w])>>> sorted([wd for wd in set(text3) if wd.istitle() and len(wd) > 10])>>> sorted([w for w in set(sent7) if not w.islower()])>>> sorted([t for t in set(text2) if 'cie' in t or 'cei' in t])

From the NLTK book: Run the following examples and explain what is happening. Then make up some tests of your own.

Ending the double count of words

The count of words from the various texts was flawed. How?We hadWhat’s the problem? How do we fix it?

>>> len(text1)260819>>> len(set(text1))19317>>> len(set([word.lower() for word in text1]))17231>>>

>>>

len(set([word.lower

() for word in text1 if

word.isalpha

()]))

16948

>>>

Nested loops and loops with conditions

Follow what happens.

>>> for token in sent1:

... if

token.islower

():

... print token, 'is a lowercase word'

...

elif

token.istitle

():

... print token, 'is a

titlecase

word'

... else:

... print token, 'is punctuation'

...

Call is a

titlecase

word

me is a lowercase word

Ishmael is a

titlecase

word

. is punctuation

>>>

Another example

>>> tricky =

sorted([w

for

w

in set(text2) if '

cie

' in

w

or '

cei

' in

w

])

>>> for word in tricky:

... print word,

ancient ceiling conceit conceited conceive conscience

conscientious conscientiously deceitful deceive ...

>>>

Automatic Text Understanding

See section 1.5Word sense disambiguation

a. The lost children were found by the searchers (agentive)b. The lost children were found by the mountain (locative)c. The lost children were found by the afternoon (temporal)

Pronoun resolution

a. The thieves stole the paintings. They were subsequently sold.

b

. The thieves stole the paintings. They were subsequently caught.

c

. The thieves stole the paintings. They were subsequently found.

Generating text!

>>> text4.generate()Building ngram index...Fellow - Citizens : Under Providence I have given freedom new reach ,and maintain lasting peace -- based on righteousness and justice .There was this reason only why the cotton - producing States should bepromoted by just and abundant society , on just principles . Theselater years have elapsed , and civil war . More than this , we affirma new beginning is a destiny . May Congress prohibit slavery in theworkshop , in translating humanity ' s strongest , but we have adopted, and fear of God . And , in each>>>

An inaugural address??

-- MIT hoax – conference submission

Translation

Babel> How long before the next flight to Alice Springs?

Babel>

german

Babel> run

0> How long before the next flight to Alice Springs?

1>

Wie

lang

vor

dem

folgenden

Flug

zu

Alice Springs?

2> How long before the following flight to Alice jump?

3>

Wie

lang

vor

dem

folgenden

Flug

zu

Alice

springen

Sie

?

4> How long before the following flight to Alice do you jump?

5>

Wie

lang

,

bevor

der

folgende

Flug

zu

Alice

tun

,

Sie

springen

?

6> How long, before the following flight to Alice does, do you jump?

7>

Wie

lang

bevor

der

folgende

Flug

zu

Alice tut,

tun

Sie

springen

?

8> How long before the following flight to Alice does, do you jump?

9>

Wie

lang

,

bevor

der

folgende

Flug

zu

Alice tut,

tun

Sie

springen

?

10> How long, before the following flight does to Alice, do do you jump?

11>

Wie

lang

bevor

der

folgende

Flug

zu

Alice tut,

Sie

tun

Sprung?

12> How long before the following flight does leap to Alice, does you?

Babel>

Jeopardy and Watson

http://www.youtube.com/watch?v=xm8iUjzgPTg&feature=related

http://www.youtube.com/watch?v=7h4baBEi0iA&feature=related -- the strange response

http://www.youtube.com/watch?src_vid=7h4baBEi0iA&feature=iv&v=lI-M7O_bRNg&annotation_id=annotation_383798#t=3m11sExplanation of the strange response

The ultimate example of a machine and language