Introduction and Course Overview

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Natural Language Processing

Julia Hirschberg

COMS 4705

Fall 2010

CS 4705

What is Natural Language Processing?

• Software that can recognize, analyze and generate text and speech

• AKA computational linguistics

• At Columbia:

– Michael Collins, CS, parsing, machine translation

– Mona Diab, CCLS, semantics

– Nizar Habash, CCLS, morphology, machine translation

– Julia Hirschberg, CS, spoken language processing

– Kathy McKeown, CS, summarization, generation

– Becky Passonneau, CCLS, dialogue systems, reference resolution

– Owen Rambow, CCLS, syntax, parsing

Why is NLP hard? Some Headlines…

Something Went Wrong In Jet Crash, Expert Says

• Police Begin Campaign To Run Down Jaywalkers

• Drunk Gets Nine Months In Violin Case

• Farmer Bill Dies In House

• Iraqi Head Seeks Arms

Enraged Cow Injures Farmer With Ax

Stud Tires Out

Eye Drops Off Shelf

• Teacher Strikes Idle Kids

• Squad Helps Dog Bite Victim

What will we learn about in this course?

• Morphology: the way words are formed

Syntax: the way words are grouped together into larger constituents and phrases and the way these phrases can be ordered

Semantics: the context-independent ‘meaning’ of utterances

Pragmatics: the context-dependent ‘meaning’ of utterances

Goal: What is a speaker/writer meaning to convey?

Morphology

• Stud tires out : Is ` stud ’ an adjective or a noun?

`tires’: a noun or a verb?

• Internet search: ` union activities in New York

– What to look for?

• Union/unions; activities/activity

• Active? Action? Actor? Actual? Academic?

• New vs. New York, York vs. yorkie

Syntax

• Constituent Structure:

Teacher Strikes Idle Kids

Enraged Cow Injures Farmer With Ax

• Word Order and Position and Meaning

John hit Bill.

Bill was hit by John.

– Bill, John hit.

Who John hit was Bill.

– I said John hit Bill.

John hits Bill.

Semantics

• Word meaning – semantic roles

John picked up a bad cold.

John picked up a large rock.

– John picked up Radio Netherlands on his radio.

• Is meaning compositional?

Squad helps dog bite victim

Enraged cow injures farmer with ax

Pragmatics

• Going Home , a play in one act (thanks to Bonnie

Dorr)

– Scene 1: Pennsylvania Station, NY

• Bonnie: Long Beach?

• Passerby: Downstairs, LIRR Station.

– Scene 2: Ticket Counter, LIRR Station

• Bonnie: Long Beach?

• Clerk: $4.50.

– Scene 3: Information Booth, LIRR Station

• Bonnie: Long Beach?

• Clerk: 4:19, Track 17.

– Scene 4: On the train, vicinity of Forest Hills

• Bonnie: Long Beach?

• Conductor: Change at Jamaica.

– Scene 5: On the next train, vicinity of Lynbrook

• Bonnie: Long Beach?

• Conductor: Right after Island Park.

Algorithms

• Rule-based

– Symbolic Parsers and morphological analyzers

– Finite state automata

Probabilistic/statistical

– Learned from observation of (labeled) data

– Predicting new data based on old

– Machine learning

Current Real-World Applications

• Search : very large corpora, e.g. Google

• Question answering : e.g. IBM’s Jeopardy!,

DARPA who/what/where…, Ask Jeeves

• Translating between one language and another: e.g. Google Translate, Babelfish

• Summarizing very large amounts of text or speech: e.g. your email, the news, voicemail

• Sentiment analysis

: restaurant or movie reviews

• Dialogue systems : e.g. Amtrak’s ‘Julie’

Instructor

Julia Hirschberg

– CEPSR 705, julia@cs.columbia.edu

– Focus: Spoken Language Processing

– Lab:

The Speech Lab , CEPSR 7LW3-A

– Research:

• Deceptive speech

• Charismatic speech:

• Emotional speech

: anger, uncertainty

• Speech summarization

: Broadcast News

• Spoken Dialogue Systems

: Games Corpus

• ` Translating Prosody ’: English – Mandarin

• Text2Scene Synthesis

Course Details

• Teaching Assistants:

– Mohamed Altantawy

• Email: ma2795@columbia.edu

• Office Hours: CEPSR 7LW1 (Speech Lab), W 5-6,

Th 5:30-6:30

• Will manage CVN course

– Wei Yun Ma

• Email: wm2174@columbia.edu

• Office Hours: CEPSR 725, Tu 10-12

• http://www1.cs.columbia.edu/~julia/courses/CS47

05/syllabus10.htm

• Text: Daniel Jurafsky and James H. Martin,

Speech and Language Processing , second edition

– Note errata available on website

• Check courseworks for additional information on class, homework assignments, posting questions

• Assignments:

– 3 homework assignments: Question-answering, text classification, delightful surprise

– Midterm and final exams

– Five ‘free’ late days for homeworks -- after that 10% off per late day– not usable on HW1 though

– You will need a CS account

Recorded Lecture Availability

• For on-campus students

– On CVN website

Grading

• HW1: 10%

• Hw2: 20%

• Hw3: 20%

• Midterm: 15%

• Final: 25%

• Class participation: 10%

Academic Integrity

Copying or paraphrasing someone's work (code included), or permitting your own work to be copied or paraphrased, even if only in part, is forbidden, and will result in an automatic grade of 0 for the entire assignment or exam in which the copying or paraphrasing was done. Your grade should reflect your own work. If you are going to have trouble completing an assignment, talk to the instructor or

TA in advance of the due date please. Everyone:

Read/write protect your homework files at all times.

For Next Class

• Look at syllabus – ask questions about anything you don’t understand

• Read Chapters 1-2 of J&M

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