cs4705 natural language processing: summing up
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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 - PowerPoint PPT PresentationTRANSCRIPT
CS 4705
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
• Studying NLP involves studying natural language, formal representations, and algorithms for their manipulation
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/
Semantics: on (mat, cats) & own (mat,cats)event: sitting
agent: catspatient: mat
Entity extraction: superior creatures [the cats] sat on their
matCollocations:WSD:
Pragmatic/Discourse:Information Status: They/DG/HG warily
watched the dog/DN/HN.
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 matSpeech recognition: A cat is set on a match.TTS: The cats sat on their mat.
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
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)
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
• Recognizing and making use of disfluencies, back-channels in ASR and understanding
Final and Papers
• Final examination: covers second half of course• Grad student papers: due at the final