CS4705 Natural Language Processing: Summing Up

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