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(Language Modeling)Introduction to N-grams

人工智能

语言模型

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(Language Modeling)Introduction to N-grams

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IntroductiontoN-grams

Language

Modeling

DanJurafsky

Probabilistic Language Models

• Today’sgoal:assignaprobabilitytoasentence

• MachineTranslation:

• P(high windstonite)>P(largewindstonite)

• SpellCorrection

• Theofficeisaboutfifteenminuetsfrommyhouse

• P(aboutfifteenminutesfrom)>P(aboutfifteenminuetsfrom)

• SpeechRecognition

• P(Isawavan)>>P(eyesaweofan)

• +Summarization,question-answering,etc.,etc.!!

Why?

DanJurafsky

Probabilistic Language Modeling

• Goal:computetheprobabilityofasentenceor

sequenceofwords:

P(W)=P(w

…w

)

• Relatedtask:probabilityofanupcomingword:

P(w

)

• Amodelthatcomputeseitherofthese:

P(W)orP(w

…w

n-1

)iscalledalanguage model.

• Better:the grammar Butlanguage model orLM isstandard

DanJurafsky

How to compute P(W)

• Howtocomputethisjointprobability:

• P(its,water,is,so,transparent,that)

• Intuition:let’srelyontheChainRuleofProbability

DanJurafsky

Reminder: The Chain Rule

• Recallthedefinitionofconditionalprobabilities

p(B|A) = P(A,B)/P(A) Rewriting:P(A,B) = P(A)P(B|A)

• Morevariables:

P(A,B,C,D)=P(A)P(B|A)P(C|A,B)P(D|A,B,C)

• TheChainRuleinGeneral

P(x

,…,x

)=P(x

)P(x

)…P(x

,…,x

n-1

)

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