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Applications (2 of 2): Recognition, Transduction, Discrimination, Segmentation, Alignment, etc.

Applications (2 of 2): Recognition, Transduction, Discrimination, Segmentation, Alignment, etc. Kenneth Church Kenneth.Church@jhu.edu. Applications. Recognition: Shannon’s Noisy Channel Model Speech, Optical Character Recognition (OCR), Spelling Transduction Part of Speech (POS) Tagging

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Applications (2 of 2): Recognition, Transduction, Discrimination, Segmentation, Alignment, etc.

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  1. Applications (2 of 2):Recognition, Transduction, Discrimination, Segmentation, Alignment, etc. Kenneth Church Kenneth.Church@jhu.edu

  2. 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

  3. Speech  LanguageShannon’s: Noisy Channel Model Channel Model Language Model • I  Noisy Channel  O • I΄ ≈ ARGMAXI Pr(I|O) = ARGMAXI Pr(I) Pr(O|I) Application Independent

  4. Speech  LanguageUsing (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)

  5. Spelling Correction

  6. Evaluation

  7. Performance

  8. The Task is Hard without Context

  9. 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

  10. Easier with Context

  11. Context Model

  12. Future Improvements • Add More Factors • Trigrams • Thesaurus Relations • Morphology • Syntactic Agreement • Parts of Speech • Improve Combination Rules • Shrink (Meaty Methodology)

  13. 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)

  14. Aligning Words

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