Embed Size (px)
Citation preview
61A Lecture 35
Wednesday, December 4
Announcements
2
Announcements
• Homework 11 due Thursday 12/5 @ 11:59pm.
2
Announcements
• Homework 11 due Thursday 12/5 @ 11:59pm.
• No video of lecture on Friday 12/6.
2
Announcements
• Homework 11 due Thursday 12/5 @ 11:59pm.
• No video of lecture on Friday 12/6.
!Come to class and take the final survey.
2
Announcements
• Homework 11 due Thursday 12/5 @ 11:59pm.
• No video of lecture on Friday 12/6.
!Come to class and take the final survey.
!There will be a screencast of live lecture (as always).
2
Announcements
• Homework 11 due Thursday 12/5 @ 11:59pm.
• No video of lecture on Friday 12/6.
!Come to class and take the final survey.
!There will be a screencast of live lecture (as always).
!Screencasts: http://www.youtube.com/view_play_list?p=-XXv-cvA_iCIEwJhyDVdyLMCiimv6Tup
2
Announcements
• Homework 11 due Thursday 12/5 @ 11:59pm.
• No video of lecture on Friday 12/6.
!Come to class and take the final survey.
!There will be a screencast of live lecture (as always).
!Screencasts: http://www.youtube.com/view_play_list?p=-XXv-cvA_iCIEwJhyDVdyLMCiimv6Tup
• Homework 12 due Tuesday 12/10 @ 11:59pm.
2
Announcements
• Homework 11 due Thursday 12/5 @ 11:59pm.
• No video of lecture on Friday 12/6.
!Come to class and take the final survey.
!There will be a screencast of live lecture (as always).
!Screencasts: http://www.youtube.com/view_play_list?p=-XXv-cvA_iCIEwJhyDVdyLMCiimv6Tup
• Homework 12 due Tuesday 12/10 @ 11:59pm.
!All you have to do is vote on your favorite recursive art.
2
Announcements
• Homework 11 due Thursday 12/5 @ 11:59pm.
• No video of lecture on Friday 12/6.
!Come to class and take the final survey.
!There will be a screencast of live lecture (as always).
!Screencasts: http://www.youtube.com/view_play_list?p=-XXv-cvA_iCIEwJhyDVdyLMCiimv6Tup
• Homework 12 due Tuesday 12/10 @ 11:59pm.
!All you have to do is vote on your favorite recursive art.
• 29 review sessions next week! Come learn about the topics that interest you the most.
2
Announcements
• Homework 11 due Thursday 12/5 @ 11:59pm.
• No video of lecture on Friday 12/6.
!Come to class and take the final survey.
!There will be a screencast of live lecture (as always).
!Screencasts: http://www.youtube.com/view_play_list?p=-XXv-cvA_iCIEwJhyDVdyLMCiimv6Tup
• Homework 12 due Tuesday 12/10 @ 11:59pm.
!All you have to do is vote on your favorite recursive art.
• 29 review sessions next week! Come learn about the topics that interest you the most.
!See http://inst.eecs.berkeley.edu/~cs61a/fa13/exams/final.html for the schedule.
2
Natural Language Processing
Ambiguity in Natural Language
Unlike programming languages, natural languages are ambiguous.
4
Ambiguity in Natural Language
Unlike programming languages, natural languages are ambiguous.
4
Syntactic ambiguity:
Ambiguity in Natural Language
Unlike programming languages, natural languages are ambiguous.
4
Syntactic ambiguity: TEACHER STRIKES IDLE KIDS
Ambiguity in Natural Language
Unlike programming languages, natural languages are ambiguous.
4
Syntactic ambiguity: TEACHER STRIKES IDLE KIDS HOSPITALS ARE SUED BY 7 FOOT DOCTORS
Ambiguity in Natural Language
Unlike programming languages, natural languages are ambiguous.
4
Syntactic ambiguity:
Semantic ambiguity:
TEACHER STRIKES IDLE KIDS HOSPITALS ARE SUED BY 7 FOOT DOCTORS
Ambiguity in Natural Language
Unlike programming languages, natural languages are ambiguous.
4
Syntactic ambiguity:
Semantic ambiguity: IRAQI HEAD SEEKS ARMS
TEACHER STRIKES IDLE KIDS HOSPITALS ARE SUED BY 7 FOOT DOCTORS
Ambiguity in Natural Language
Unlike programming languages, natural languages are ambiguous.
4
Syntactic ambiguity:
Semantic ambiguity: IRAQI HEAD SEEKS ARMS
TEACHER STRIKES IDLE KIDS HOSPITALS ARE SUED BY 7 FOOT DOCTORS
STOLEN PAINTING FOUND BY TREE
Tasks in Natural Language Processing
5
Tasks in Natural Language Processing
Research in natural language processing (NLP) focuses on tasks that involve language:
5
Tasks in Natural Language Processing
Research in natural language processing (NLP) focuses on tasks that involve language:
Question answering. "Harriet Boyd Hawes was the first woman to discover and excavate a Minoan settlement on this island." Watson says, "What is Crete?"
5
Tasks in Natural Language Processing
Research in natural language processing (NLP) focuses on tasks that involve language:
Question answering. "Harriet Boyd Hawes was the first woman to discover and excavate a Minoan settlement on this island." Watson says, "What is Crete?"
Machine Translation. "Call a spade a spade!" Google Translate says, "Appeler un chat un chat."
5
Tasks in Natural Language Processing
Research in natural language processing (NLP) focuses on tasks that involve language:
Question answering. "Harriet Boyd Hawes was the first woman to discover and excavate a Minoan settlement on this island." Watson says, "What is Crete?"
Machine Translation. "Call a spade a spade!" Google Translate says, "Appeler un chat un chat."
Semantic Parsing. "When's my birthday?" Siri says, "Your birthday is May 1st."
5
Tasks in Natural Language Processing
Research in natural language processing (NLP) focuses on tasks that involve language:
Question answering. "Harriet Boyd Hawes was the first woman to discover and excavate a Minoan settlement on this island." Watson says, "What is Crete?"
Machine Translation. "Call a spade a spade!" Google Translate says, "Appeler un chat un chat."
Semantic Parsing. "When's my birthday?" Siri says, "Your birthday is May 1st."
Much attention is given to more focused language analysis problems:
5
Tasks in Natural Language Processing
Research in natural language processing (NLP) focuses on tasks that involve language:
Question answering. "Harriet Boyd Hawes was the first woman to discover and excavate a Minoan settlement on this island." Watson says, "What is Crete?"
Machine Translation. "Call a spade a spade!" Google Translate says, "Appeler un chat un chat."
Semantic Parsing. "When's my birthday?" Siri says, "Your birthday is May 1st."
Much attention is given to more focused language analysis problems:
Coreference Resolution: Do the phrases "Barack Obama" and "the president" co-refer?
5
Tasks in Natural Language Processing
Research in natural language processing (NLP) focuses on tasks that involve language:
Question answering. "Harriet Boyd Hawes was the first woman to discover and excavate a Minoan settlement on this island." Watson says, "What is Crete?"
Machine Translation. "Call a spade a spade!" Google Translate says, "Appeler un chat un chat."
Semantic Parsing. "When's my birthday?" Siri says, "Your birthday is May 1st."
Much attention is given to more focused language analysis problems:
Coreference Resolution: Do the phrases "Barack Obama" and "the president" co-refer?
Syntactic Parsing: In "I saw the man with the telescope," who has the telescope?
5
Tasks in Natural Language Processing
Research in natural language processing (NLP) focuses on tasks that involve language:
Question answering. "Harriet Boyd Hawes was the first woman to discover and excavate a Minoan settlement on this island." Watson says, "What is Crete?"
Machine Translation. "Call a spade a spade!" Google Translate says, "Appeler un chat un chat."
Semantic Parsing. "When's my birthday?" Siri says, "Your birthday is May 1st."
Much attention is given to more focused language analysis problems:
Coreference Resolution: Do the phrases "Barack Obama" and "the president" co-refer?
Syntactic Parsing: In "I saw the man with the telescope," who has the telescope?
Word Sense Disambiguation: Does the "bank of the Seine" have an ATM?
5
Tasks in Natural Language Processing
Research in natural language processing (NLP) focuses on tasks that involve language:
Question answering. "Harriet Boyd Hawes was the first woman to discover and excavate a Minoan settlement on this island." Watson says, "What is Crete?"
Machine Translation. "Call a spade a spade!" Google Translate says, "Appeler un chat un chat."
Semantic Parsing. "When's my birthday?" Siri says, "Your birthday is May 1st."
Much attention is given to more focused language analysis problems:
Coreference Resolution: Do the phrases "Barack Obama" and "the president" co-refer?
Syntactic Parsing: In "I saw the man with the telescope," who has the telescope?
Word Sense Disambiguation: Does the "bank of the Seine" have an ATM?
Named-Entity Recognition: What names are in "Did van Gogh paint the Bank of the Seine?"
5
Machine Translation
Machine Translation
7
Machine Translation
Target language corpus gives examples of well-formed sentences
I will get to it later See you later He will do it
7
Machine Translation
Parallel corpus gives translation examples
Yo lo haré de muy buen grado
I will do it gladly
Después lo veras
You will see later
Target language corpus gives examples of well-formed sentences
I will get to it later See you later He will do it
7
Machine Translation
Parallel corpus gives translation examples
Yo lo haré de muy buen grado
I will do it gladly
Después lo veras
You will see later
Machine translation system:
Target language corpus gives examples of well-formed sentences
I will get to it later See you later He will do it
7
Machine Translation
Parallel corpus gives translation examples
Yo lo haré de muy buen grado
I will do it gladly
Después lo veras
You will see later
Machine translation system:
Model of translation
Target language corpus gives examples of well-formed sentences
I will get to it later See you later He will do it
7
Machine Translation
I will do it later
Target language
Parallel corpus gives translation examples
Yo lo haré de muy buen grado
I will do it gladly
Después lo veras
You will see later
Machine translation system:
Model of translation
Target language corpus gives examples of well-formed sentences
I will get to it later See you later He will do it
Yo lo haré despuésNOVEL SENTENCE
Source language
7
Syntactic Agreement in Translation
VB
MD VP
VPNP
S
PRP ADV
Yo lo haré de muy buen grado
I will do it gladly
Después lo veras
You will see later
PRPVB
MD VP
VPNP
S
PRP ADV
I will do it laterModel of translation
Yo lo haré después
Machine translation system:
8
Syntactic Agreement in Translation
VB
MD VP
VPNP
S
PRP ADV
Yo lo haré de muy buen grado
I will do it gladly
Después lo veras
You will see later
PRPVB
MD VP
VPNP
S
PRP ADV
I will do it laterModel of translation
Yo lo haré despuésADV ADV
Machine translation system:
8
Syntactic Agreement in Translation
VB
MD VP
VPNP
S
PRP ADV
Yo lo haré de muy buen grado
I will do it gladly
Después lo veras
You will see later
PRPVB
MD VP
VPNP
S
PRP ADV
I will do it laterModel of translation
Yo lo haré después
S SADV ADV
Machine translation system:
8
Syntactic Agreement in Translation
VB
MD VP
VPNP
S
PRP ADV
Yo lo haré de muy buen grado
I will do it gladly
Después lo veras
You will see later
PRPVB
MD VP
VPNP
S
PRP ADV
I will do it laterModel of translation
Yo lo haré después
S SADV ADV
Machine translation system:
8
Syntactic Reordering in Translation
9
pair added to the lexicon
Syntactic Reordering in Translation
9
pair added to the lexicon
NP
PP
VP
NP
S
NN VBD TO DT NN
Syntactic Reordering in Translation
9
pair added to the lexicon
NP
PP
VP
NP
S
NN VBD TO DT NN
pair
NP
S
NN
Syntactic Reordering in Translation
9
pair added to the lexicon
NP
PP
VP
NP
S
NN VBD TO DT NN
pair
NP
S
NN
VP
added
VBD
Syntactic Reordering in Translation
9
pair added to the lexicon
NP
PP
VP
NP
S
NN VBD TO DT NN
to
PP
TO
pair
NP
S
NN
VP
added
VBD
Syntactic Reordering in Translation
9
pair added to the lexicon
NP
PP
VP
NP
S
NN VBD TO DT NN
to
PP
TO
pair
NP
S
NN
the lexicon
NP
DT NN
VP
added
VBD
Syntactic Reordering in Translation
9
pair added to the lexicon
NP
PP
VP
NP
S
NN VBD TO DT NN
to
PP
TO
pair
NP
S
NN
the lexicon
NP
DT NN
VP
added
VBD
Syntactic Reordering in Translation
9
pair added to the lexicon
NP
PP
VP
NP
S
NN VBD TO DT NN
to
PP
TO
pair
NP
S
NN
the lexicon
NP
DT NN
VP
added
VBD
Syntactic Reordering in Translation
9
pair added to the lexicon
NP
PP
VP
NP
S
NN VBD TO DT NN
to
PP
TO
pair
NP
S
NN
the lexicon
NP
DT NN
VP
added
VBD
Syntactic Reordering in Translation
9
pair added to the lexicon
NP
PP
VP
NP
S
NN VBD TO DT NN
to
PP
TO
pair
NP
S
NN
the lexicon
NP
DT NN
VP
added
VBD
Syntactic Reordering in Translation
9
pair added to the lexicon
NP
PP
VP
NP
S
NN VBD TO DT NN
to
PP
TO
一対 がpair
pair
NP
S
NN
the lexicon
NP
DT NN
VP
added
VBD
Syntactic Reordering in Translation
9
pair added to the lexicon
NP
PP
VP
NP
S
NN VBD TO DT NN
to
PP
TO
一対 がpair
目録list
pair
NP
S
NN
the lexicon
NP
DT NN
VP
added
VBD
Syntactic Reordering in Translation
9
pair added to the lexicon
NP
PP
VP
NP
S
NN VBD TO DT NN
to
PP
TO
一対 がpair
目録list
にto
pair
NP
S
NN
the lexicon
NP
DT NN
VP
added
VBD
Syntactic Reordering in Translation
9
pair added to the lexicon
NP
PP
VP
NP
S
NN VBD TO DT NN
to
PP
TO
一対 がpair
目録list
にto
追加されましたadd was
pair
NP
S
NN
the lexicon
NP
DT NN
VP
added
VBD
Context-Free Grammars
Grammar Rules
A Context-Free Grammar Models Language Generation
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
Grammar Rules
A Context-Free Grammar Models Language Generation
S
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
Grammar Rules
A Context-Free Grammar Models Language Generation
S
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
Grammar Rules
A Context-Free Grammar Models Language Generation
S
NP VP
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
NP -> PRP
Grammar Rules
A Context-Free Grammar Models Language Generation
S
NP VP
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
NP -> PRP
Grammar Rules
A Context-Free Grammar Models Language Generation
S
NP VP
PRP
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
NP -> PRP
Grammar Rules
Lexicon
A Context-Free Grammar Models Language Generation
S
NP VP
PRP
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
NP -> PRP
Grammar Rules
Lexicon
PRP -> I
A Context-Free Grammar Models Language Generation
S
NP VP
PRP
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
NP -> PRP
Grammar Rules
Lexicon
PRP -> I
A Context-Free Grammar Models Language Generation
S
NP VP
PRP
I
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
NP -> PRP
VP -> VB
Grammar Rules
Lexicon
PRP -> I
A Context-Free Grammar Models Language Generation
S
NP VP
PRP
I
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
NP -> PRP
VP -> VB
VP -> VB NP
Grammar Rules
Lexicon
PRP -> I
A Context-Free Grammar Models Language Generation
S
NP VP
PRP
I
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
NP -> PRP
VP -> VB
VP -> VB NP
Grammar Rules
Lexicon
PRP -> I
A Context-Free Grammar Models Language Generation
S
NP VP
PRP VB NP
I
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
NP -> PRP
VP -> VB
VP -> VB NP
VB -> know
VB -> help
Grammar Rules
Lexicon
PRP -> I
A Context-Free Grammar Models Language Generation
S
NP VP
PRP VB NP
I
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
NP -> PRP
VP -> VB
VP -> VB NP
VB -> know
VB -> help
Grammar Rules
Lexicon
PRP -> I
A Context-Free Grammar Models Language Generation
S
NP VP
PRP VB NP
I know
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
NP -> PRP
VP -> VB
VP -> VB NP
VB -> know
VB -> help
Grammar Rules
Lexicon
PRP -> I
A Context-Free Grammar Models Language Generation
S
NP VP
PRP VB NP
I know PRP
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
NP -> PRP
VP -> VB
VP -> VB NP
PRP -> you
VB -> know
VB -> help
Grammar Rules
Lexicon
PRP -> I
A Context-Free Grammar Models Language Generation
S
NP VP
PRP VB NP
I know PRP
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
S -> NP VP
NP -> PRP
VP -> VB
VP -> VB NP
PRP -> you
VB -> know
VB -> help
Grammar Rules
Lexicon
PRP -> I
A Context-Free Grammar Models Language Generation
S
NP VP
PRP VB NP
I know PRP
you
A grammar contains rules that hierarchically generate word sequences using syntactic tags.
11
Probabilistic Context-Free Grammars
S -> NP VP
NP -> PRP
PRP -> I
VP -> VB
VP -> VB NP
PRP -> you
VB -> know
VB -> help
Grammar Rules
Lexicon
S
NP VP
PRP
I
12
Probabilistic Context-Free Grammars
S -> NP VP
NP -> PRP
PRP -> I
VP -> VB
VP -> VB NP
PRP -> you
VB -> know
VB -> help
Grammar Rules
Lexicon
S
NP VP
PRP
I
12
Probabilistic Context-Free Grammars
S -> NP VP
NP -> PRP
PRP -> I
VP -> VB
VP -> VB NP
PRP -> you
VP -> MD VP
VB -> know
VB -> help
Grammar Rules
Lexicon
S
NP VP
PRP
I
12
Probabilistic Context-Free Grammars
S -> NP VP
NP -> PRP
PRP -> I
VP -> VB
VP -> VB NP
PRP -> you
VP -> MD VP
VB -> know
VB -> help
Grammar Rules
Lexicon
S
NP VP
PRP
I
12
Probabilistic Context-Free Grammars
S -> NP VP
NP -> PRP
PRP -> I
VP -> VB
VP -> VB NP
PRP -> you
VP -> MD VP
VB -> know
VB -> help
Grammar Rules
Lexicon
S
NP VP
PRP
I
0.2
0.7
0.1
12
Probabilistic Context-Free Grammars
S -> NP VP
NP -> PRP
PRP -> I
VP -> VB
VP -> VB NP
PRP -> you
VP -> MD VP
VB -> know
VB -> help
Grammar Rules
Lexicon
S
NP VP
PRP
I
MD VP
0.2
0.7
0.1
12
Probabilistic Context-Free Grammars
S -> NP VP
NP -> PRP
PRP -> I
VP -> VB
VP -> VB NP
PRP -> you
VP -> MD VP
VB -> know
VB -> help
MD -> can
Grammar Rules
Lexicon
S
NP VP
PRP
I
MD VP
0.2
0.7
0.1
12
Probabilistic Context-Free Grammars
S -> NP VP
NP -> PRP
PRP -> I
VP -> VB
VP -> VB NP
PRP -> you
VP -> MD VP
VB -> know
VB -> help
MD -> can
Grammar Rules
Lexicon
S
NP VP
PRP
I can
MD VP
0.2
0.7
0.1
12
Probabilistic Context-Free Grammars
S -> NP VP
NP -> PRP
PRP -> I
VP -> VB
VP -> VB NP
PRP -> you
VP -> MD VP
VB -> know
VB -> help
MD -> can
Grammar Rules
Lexicon
S
NP VP
PRP
I can
MD VP
0.2
0.7
0.1
12
Probabilistic Context-Free Grammars
S -> NP VP
NP -> PRP
PRP -> I
VP -> VB
VP -> VB NP
PRP -> you
VP -> MD VP
VB -> know
VB -> help
MD -> can
Grammar Rules
Lexicon
S
NP VP
PRP
I can
MD VP
VB NP
help
0.2
0.7
0.1
12
Probabilistic Context-Free Grammars
S -> NP VP
NP -> PRP
PRP -> I
VP -> VB
VP -> VB NP
PRP -> you
VP -> MD VP
VB -> know
VB -> help
MD -> can
Grammar Rules
Lexicon
S
NP VP
PRP
I can
MD VP
VB NP
help PRP
0.2
0.7
0.1
12
Probabilistic Context-Free Grammars
S -> NP VP
NP -> PRP
PRP -> I
VP -> VB
VP -> VB NP
PRP -> you
VP -> MD VP
VB -> know
VB -> help
MD -> can
Grammar Rules
Lexicon
S
NP VP
PRP
I can
MD VP
VB NP
help PRP
you
0.2
0.7
0.1
12
Learning Probabilistic Context-Free Grammars
(Demo)
Parsing with Probabilistic Context-Free Grammars
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
time flies like an arrow
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
time flies like an arrow
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
time flies like an arrow
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananastime flies like an arrow
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananastime flies like an arrow
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananastime flies like an arrow
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananas
Parse by finding the tree with the highest total probability that yields the sentence.
time flies like an arrow
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananas
Parse by finding the tree with the highest total probability that yields the sentence.
time flies like an arrow
Algorithm: Try every rule over every span. Match the lexicon to each word.
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananas
Parse by finding the tree with the highest total probability that yields the sentence.
time flies like an arrow0 1 2 3 4 5
time flies like an arrow
Algorithm: Try every rule over every span. Match the lexicon to each word.
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananas
Parse by finding the tree with the highest total probability that yields the sentence.
time flies like an arrow0 1 2 3 4 5
time flies like an arrow
Algorithm: Try every rule over every span. Match the lexicon to each word.
S -> NP VP
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananas
Parse by finding the tree with the highest total probability that yields the sentence.
time flies like an arrow0 1 2 3 4 5
time flies like an arrow
Algorithm: Try every rule over every span. Match the lexicon to each word.
S -> NP VP
NP -> NN
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananas
Parse by finding the tree with the highest total probability that yields the sentence.
time flies like an arrow0 1 2 3 4 5
time flies like an arrow
Algorithm: Try every rule over every span. Match the lexicon to each word.
NN -> time
S -> NP VP
NP -> NN
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananas
Parse by finding the tree with the highest total probability that yields the sentence.
time flies like an arrow0 1 2 3 4 5
time flies like an arrow
Algorithm: Try every rule over every span. Match the lexicon to each word.
NN -> time
S -> NP VP
NP -> NN VP -> VBZ PP
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananas
Parse by finding the tree with the highest total probability that yields the sentence.
time flies like an arrow0 1 2 3 4 5
time flies like an arrow
Algorithm: Try every rule over every span. Match the lexicon to each word.
NN -> time VBZ -> flies
S -> NP VP
NP -> NN VP -> VBZ PP
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananas
Parse by finding the tree with the highest total probability that yields the sentence.
time flies like an arrow0 1 2 3 4 5
time flies like an arrow
Algorithm: Try every rule over every span. Match the lexicon to each word.
NN -> time VBZ -> flies
S -> NP VP
NP -> NN VP -> VBZ PP
PP -> IN NP
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananas
Parse by finding the tree with the highest total probability that yields the sentence.
time flies like an arrow0 1 2 3 4 5
time flies like an arrow
Algorithm: Try every rule over every span. Match the lexicon to each word.
NN -> time VBZ -> flies IN -> like
S -> NP VP
NP -> NN VP -> VBZ PP
PP -> IN NP
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananas
Parse by finding the tree with the highest total probability that yields the sentence.
time flies like an arrow0 1 2 3 4 5
time flies like an arrow
Algorithm: Try every rule over every span. Match the lexicon to each word.
NN -> time VBZ -> flies IN -> like
S -> NP VP
NP -> NN VP -> VBZ PP
PP -> IN NP
NP -> DT NN
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananas
Parse by finding the tree with the highest total probability that yields the sentence.
time flies like an arrow0 1 2 3 4 5
time flies like an arrow
Algorithm: Try every rule over every span. Match the lexicon to each word.
NN -> time VBZ -> flies IN -> like DT -> an
S -> NP VP
NP -> NN VP -> VBZ PP
PP -> IN NP
NP -> DT NN
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananas
Parse by finding the tree with the highest total probability that yields the sentence.
time flies like an arrow0 1 2 3 4 5
time flies like an arrow
Algorithm: Try every rule over every span. Match the lexicon to each word.
NN -> time VBZ -> flies IN -> like DT -> an NN -> arrow
S -> NP VP
NP -> NN VP -> VBZ PP
PP -> IN NP
NP -> DT NN
15
Parsing is Maximizing Likelihood
A probabilistic context-free grammar can be used to select a parse for a sentence.
fruit flies like bananas
Parse by finding the tree with the highest total probability that yields the sentence.
time flies like an arrow0 1 2 3 4 5
time flies like an arrow
Algorithm: Try every rule over every span. Match the lexicon to each word.
NN -> time VBZ -> flies IN -> like DT -> an NN -> arrow
S -> NP VP
NP -> NN VP -> VBZ PP
PP -> IN NP
NP -> DT NN
(Demo)
15
Tree Transformations
Reordering Modal Arguments
17
Reordering Modal Arguments
English
17
Reordering Modal Arguments
English Yoda-English
17
Reordering Modal Arguments
English Yoda-English Help you, I can! Yes! Mm!
17
Reordering Modal Arguments
English Yoda-English Help you, I can! Yes! Mm!
When 900 years old you reach, look as good, you will not. Hm.
17
Reordering Modal Arguments
English Yoda-English Help you, I can! Yes! Mm!
When 900 years old you reach, look as good, you will not. Hm.
S
NP VP
PRP
I can
MD VP
VB PRP
help you
17
Reordering Modal Arguments
English Yoda-English Help you, I can! Yes! Mm!
When 900 years old you reach, look as good, you will not. Hm.
S
NP VP
PRP
I can
MD VP
VB PRP
help you
17
Reordering Modal Arguments
English Yoda-English Help you, I can! Yes! Mm!
When 900 years old you reach, look as good, you will not. Hm.
S
NP VP
PRP
I can
MDVB PRP
help you
VP
17
Reordering Modal Arguments
English Yoda-English Help you, I can! Yes! Mm!
When 900 years old you reach, look as good, you will not. Hm.
S
NP VP
PRP
I can
MDVB PRP
help you
VP .
,
17
Reordering Modal Arguments
English Yoda-English Help you, I can! Yes! Mm!
When 900 years old you reach, look as good, you will not. Hm.
S
NP VP
PRP
I can
MDVB PRP
help you
VP .
,
(Demo)
17
