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14. Understanding Natural Language. 14.0The Natural Language Understanding Problem 14.1Deconstructing Language: A Symbolic Analysis 14.2Syntax 14.3Syntax and Knowledge with ATN parsers. 14.4Stochastic Tools for Language Analysis 14.5Natural Language Applications - PowerPoint PPT Presentation
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Understanding Natural Language14 14.0 The Natural Language
UnderstandingProblem
14.1 Deconstructing Language: A SymbolicAnalysis
14.2 Syntax
14.3 Syntax and Knowledgewith ATN parsers
14.4 Stochastic Tools forLanguage Analysis
14.5 Natural LanguageApplications
14.6 Epilogue and References
14.7 Exercises
Additional source used in preparing the slides:Patrick H. Winston’s AI textbook, Addison Wesley, 1993.
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Alternative to CSGs
• Retain simple structure of CFGs
• Augment them with procedures that perform the necessary contextual tests
• Attach features to terminals and nonterminals:augmented transition networks
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Parsing with ATNs
• Attach procedures to the arcs of the network.
• The parser will execute those procedures while traversing the arcs.
• The procedures: Perform tests
Construct a parse tree
• Both terminals and nonterminals are represented as identifiers with attached features.
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Frames for three nonterminals
Sentence
Noun phrase:
Verb phrase:
Verb phrase
Verb:
Number:
Object:
Noun phrase
Determiner:
Noun:
Number:
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Dictionary entries - 1
PART_OF_SPEECH: verb
ROOT: like
NUMBER: plural
like
PART_OF_SPEECH: verb
ROOT: like
NUMBER: singular
PART_OF_SPEECH: verb
ROOT: bite
NUMBER: plural
PART_OF_SPEECH: verb
ROOT: bite
NUMBER: singular
bite bites
likes
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Dictionary entries - 2
PART_OF_SPEECH: noun
ROOT: man
NUMBER: plural
men
PART_OF_SPEECH: noun
ROOT: man
NUMBER: singular
PART_OF_SPEECH: noun
ROOT: dog
NUMBER: plural
PART_OF_SPEECH: noun
ROOT: dog
NUMBER: singular
dogs dog
man
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Dictionary entries - 3
PART_OF_SPEECH: article
ROOT: a
NUMBER: singular
a
PART_OF_SPEECH: article
ROOT: the
NUMBER: plural or singular
the
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Sentence
function sentence-1; begin NOUN_PHRASE := structure returned by
noun_phrase network; SENTENCE.SUBJECT := NOUN_PHRASE; end.
init final
noun_phrase verb_phrase
1 2
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Sentence
function sentence-2; begin VERB_PHRASE := structure returned by verb_phrase network; if NOUN_PHRASE.NUMBER = VERB_PHRASE.number then begin SENTENCE.VERB_PHRASE := VERB_PHRASE; return SENTENCE end else fail end.
init final
noun_phrase verb_phrase
1 2
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Noun_phrase
function noun_phrase-1; begin ARTICLE := definition frame for next word of input; if ARTICLE.PART_OF_SPEECH = article then NOUN_PHRASE.DETERMINER := ARTICLE else fail end.
init final
article noun
1 2
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noun
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Noun_phrase
function noun_phrase-2; begin NOUN := definition frame for next word of input; if NOUN.PART_OF_SPEECH = noun and NOUN.NUMBER agrees with NOUN_PHRASE.DETERMINER.NUMBER then begin NOUN_PHRASE.NOUN := NOUN NOUN_PHRASE.NUMBER := NOUN.NUMBER return NOUN_PHRASE end else fail end.
init final
article noun
1 2
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noun
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Noun_phrase
function noun_phrase-3; begin NOUN := definition frame for next word of input; if NOUN.PART_OF_SPEECH = noun then begin NOUN_PHRASE.DETERMINER := unspecified NOUN_PHRASE.NOUN := NOUN NOUN_PHRASE.NUMBER := NOUN.NUMBER end else fail end.
init final
article noun
1 2
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noun
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Verb_phrase
function verb_phrase-1; begin VERB := definition frame for next word of input; if VERB.PART_OF_SPEECH = verb then begin VERB_PHRASE.VERB := VERB; VERB_PHRASE.NUMBER := VERB.NUMBER end; end.
init final
verb noun_phrase
1 2
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verb
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Verb_phrase
function verb_phrase-2; begin NOUN_PHRASE := structure returned by noun_phrase network; VERB.PHRASE.OBJECT := NOUN_PHRASE; return VERB_PHRASE end.
init final
verb noun_phrase
1 2
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verb
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Verb_phrase
function verb_phrase-3; begin VERB := definition frame for next word of input; if VERB.PART_OF_SPEECH = verb then begin VERB_PHRASE.VERB := VERB; VERB_PHRASE.NUMBER := VERB.NUMBER VERB_PHRASE.OBJECT := unspecified; return VERB_PHRASE end; end.
init final
verb noun_phrase
1 2
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verb
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“The dog likes a man.”
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Knowledge base for the “dogs world.”
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Case frames for two verbs
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Semantic representation
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Concluding remarks
• The parse tree can be converted to a conceptual graph that represents the meaning of the sentence.
• Simpler approaches use templates and plug in words as they are recognized.
• Applications include: Story understanding and question answering
Front End to a database
Information extraction and summarization on the web