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CS4705 Natural Language Processing: Summing Up

CS4705 Natural Language Processing: Summing Up. What is Natural Language Processing?. The study of human languages and how they can be represented computationally and analyzed , recognized , and generated algorithmically

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CS4705 Natural Language Processing: Summing Up

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  1. CS4705 Natural Language Processing: Summing Up CS 4705

  2. What is Natural Language Processing? • The study of human languages and how they can be represented computationally and analyzed, recognized, and generated algorithmically • Studying NLP involves studying natural language, formal representations, and algorithms for their manipulation

  3. The cats sat on their mat. Syntax: [S [NP [ Det [the]] [Nom [cats]]] [VP [V [sat]] [PP [Prep [on]] [NP [Det [their]] [Nom [mat]]]]]] the/DET cats/N sat/VBD on/Prep their/Pro mat/N [^ the][the cats] [cats sat] [sat on] [on their] [their mat] [mat $] Morphology: the cat+pl sit+past on pro+pl+poss mat+sing Phonology: /dhe kaetz saet ahn dhEr maet/

  4. Semantics: on (mat, cats) & own (mat,cats) event: sitting agent: cats patient: mat Entity extraction: superior creatures[the cats] sat on their mat Collocations: WSD: Pragmatic/Discourse: Information Status:They/DG/HG warily watched the dog/DN/HN.

  5. Discourse Structure: DS1[The cats sat on their mat.] DS2[They warily watched the dog.] Nuc1[The cats sat on their mat.] Nuc2[They warily watched the dog.] Sequence(Nuc,Nuc2) Reference: their [cats], they [cats] Cp=cats, Cf={cats,mat}, Cb={} Applications: IR: cat mat Speech recognition: A cat is set on a match. TTS: The cats sat on their mat.

  6. Spoken Dialogue Systems: A: Meow? B: Meooooowww… Story Generation: There was once a lonely cat. She was looking for a nice, trusting mouse. MT: Había una vez un gato solo. Summarization: A cat looked for a mouse

  7. NLP Applications • Speech Synthesis • Dialogue Systems • Text (Eliza) • Spoken (TOOT) • Machine Translation (SYSTRAN) • Nice Dr. Fish works on a bank of the Rhone River. • Summarization (NewsBlaster)

  8. Grand Challenges • Faster, more accurate ‘real’ parsing • Richer POS tagging and ‘shallow’ parsing • New semantic representations • Data Mining in text and speech e.g. “find friends”: X’s long time associate Y, X and Y have been friends, X intimate Y,… • Extracting more entity types with less labeling • Emotional Speech recognition and production • Self-paced language instruction that uses ASR and TTS

  9. Recognizing and making use of disfluencies, back-channels in ASR and understanding

  10. Final and Papers • Final examination: covers second half of course • Grad student papers: due at the final

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