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Parallel Tools for Natural Language Processing

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Parallel Tools for Natural Language Processing. Mark Brigham Melanie Goetz Andrew Hogue. 6.338 / 18.337 - March 16, 2004. Sentence Parsing. Consider the sentence: “John ate the cookie on the table” We want to: Tag the sentence with parts of speech Group the words by phrase. - PowerPoint PPT Presentation
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Parallel Tools for Natural Language Processing Mark Brigham Melanie Goetz Andrew Hogue 6.338 / 18.337 - March 16, 2004
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Page 1: Parallel Tools for Natural Language Processing

Parallel Tools for Natural Language Processing

Mark Brigham

Melanie Goetz

Andrew Hogue

6.338 / 18.337 - March 16, 2004

Page 2: Parallel Tools for Natural Language Processing

• Consider the sentence:

“John ate the cookie on the table”

• We want to:– Tag the sentence with parts of speech– Group the words by phrase

Sentence Parsing

Page 3: Parallel Tools for Natural Language Processing

Context Free Grammars

• Recursive set of rules

• Defines what syntactic structure can be applied to a phrase or word

• Top-level rule S defines the sentence

S → NP VP

NP → Det N

NP → NP PP

VP → VP PP

VP → V NP

N → ‘cookie’

N → ‘table’

Det → ‘the’

V → ‘ate’

Page 4: Parallel Tools for Natural Language Processing

Context Free Grammars

• Applying a CFG to a sentence creates a parse-tree for that sentence

Page 5: Parallel Tools for Natural Language Processing

Context Free Grammars

Top-downparse

Page 6: Parallel Tools for Natural Language Processing

Context Free Grammars

Bottom-upparse

Parallelizable!

Page 7: Parallel Tools for Natural Language Processing

Ambiguity

More than one parse for a single sentence!

Page 8: Parallel Tools for Natural Language Processing

Parallelization

• Bottom-up rule application appropriate for parallel processing

• Ambiguous parses also parallelizable

• Long, complex sentences may be most interesting

• Proust?

Page 9: Parallel Tools for Natural Language Processing

Chart Parsing

• Create a matrix where entries correspond to words/phrases

• If there is a valid CFG parse of a phrase [i,j], add it to that matrix cell

• A cell [i,j] may only depend on other cells [m,n] where m < i and n < j.

Page 10: Parallel Tools for Natural Language Processing

John ate the cookie on the table

John

ate

the

cookie

on

the

table

Page 11: Parallel Tools for Natural Language Processing

John ate the cookie on the table

John

ate

the

cookie

on

the

table

Page 12: Parallel Tools for Natural Language Processing

John ate the cookie on the table

John

ate

the

cookie

on

the

table

Page 13: Parallel Tools for Natural Language Processing

John ate the cookie on the table

John

ate

the

cookie

on

the

table

Page 14: Parallel Tools for Natural Language Processing

John ate the cookie on the table

John

ate

the

cookie

on

the

table

Page 15: Parallel Tools for Natural Language Processing

John ate the cookie on the table

John

ate

the

cookie

on

the

table

Page 16: Parallel Tools for Natural Language Processing

John ate the cookie on the table

John

ate

the

cookie

on

the

table

Page 17: Parallel Tools for Natural Language Processing

John ate the cookie on the table

John

ate

the

cookie

on

the

table

Page 18: Parallel Tools for Natural Language Processing

John ate the cookie on the table

John

ate

the

cookie

on

the

table

Page 19: Parallel Tools for Natural Language Processing

John ate the cookie on the table

John

ate

the

cookie

on

the

table

Page 20: Parallel Tools for Natural Language Processing

John ate the cookie on the table

John

ate

the

cookie

on

the

table

Page 21: Parallel Tools for Natural Language Processing

Other Tools

• Considering parallelizing other NLP tools

• Word-stemming: Multiple finite state automata applied to a single word in parallel

• Automated part-of-speech recognition on large corpora


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