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Applications (2 of 2): Recognition, Transduction, Discrimination, Segmentation, Alignment, etc. Kenneth Church [email protected]. Applications. Recognition: Shannon’s Noisy Channel Model Speech, Optical Character Recognition (OCR), Spelling Transduction Part of Speech (POS) Tagging - PowerPoint PPT Presentation
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Applications (2 of 2):Recognition, Transduction, Discrimination,
Segmentation, Alignment, etc.
Kenneth [email protected]
Dec 9, 2009
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Applications• Recognition: Shannon’s Noisy Channel Model
– Speech, Optical Character Recognition (OCR), Spelling• Transduction
– Part of Speech (POS) Tagging– Machine Translation (MT)
• Parsing: ???• Ranking
– Information Retrieval (IR)– Lexicography
• Discrimination:– Sentiment, Text Classification, Author Identification, Word Sense Disambiguation (WSD)
• Segmentation– Asian Morphology (Word Breaking), Text Tiling
• Alignment: Bilingual Corpora, Dotplots• Compression• Language Modeling: good for everything
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Speech LanguageShannon’s: Noisy Channel Model
• I Noisy Channel O• I΄ ≈ ARGMAXI Pr(I|O) = ARGMAXI Pr(I) Pr(O|I)Trigram Language Model
Word Rank More likely alternatives
We 9 The This One Two A Three Please In
need 7 are will the would also do
to 1
resolve 85 have know do…
all 9 The This One Two A Three Please In
of 2 The This One Two A Three Please In
the 1
important 657 document question first…
issues 14 thing point to
Channel Model
Application Input Output
Speech Recognition writer rider
OCR (Optical Character Recognition)
all a1l
Spelling Correction government goverment
ChannelModel
LanguageModel
ApplicationIndependent
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Speech Language Using (Abusing) Shannon’s Noisy Channel Model: Part of Speech
Tagging and Machine Translation
• Speech– Words Noisy Channel Acoustics
• OCR– Words Noisy Channel Optics
• Spelling Correction– Words Noisy Channel Typos
• Part of Speech Tagging (POS): – POS Noisy Channel Words
• Machine Translation: “Made in America”– English Noisy Channel French
Didn’t have the guts to use this slide at Eurospeech (Geneva)Dec 9, 2009
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Spelling Correction
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Evaluation
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Performance
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The Task is Hard without Context
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Easier with Context
• actuall, actual, actually– … in determining whether the defendant actually
will die.• constuming, consuming, costuming• conviced, convicted, convinced• confusin, confusing, confusion• workern, worker, workers
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Easier with Context
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Context Model
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Future Improvements
• Add More Factors– Trigrams– Thesaurus Relations– Morphology– Syntactic Agreement– Parts of Speech
• Improve Combination Rules– Shrink (Meaty Methodology)
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Conclusion (Spelling Correction)
• There has been a lot of interest in smoothing– Good-Turing estimation– Knesser-Ney
• Is it worth the trouble?• Ans: Yes (at least for recognition applications)
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Aligning Words
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