I256

Applied Natural Language Processing

Fall 2009

Lecture 1

Introduction

Barbara Rosario

Introductions

• Barbara Rosario– iSchool alumni (class 2005)– Intel Labs

• Gopal Vaswani – iSchool master student (class 2010)

• You?

Today

• Introductions• Administrivia• What is NLP• NLP Applications• Why is NLP difficult• Corpus-based statistical approaches• Course goals• What we’ll do in this course

Administrivia

• http://courses.ischool.berkeley.edu/i256/f09/index.html• Books:

– Foundations of Statistical NLP, Manning and Schuetze, MIT press– Natural Language Processing with Python, Bird, Klein & Loper,

O'Reilly.  (also on line)– See Web site for additional resources

• Work:– Individual coding assignments (Python & NLTK-Natural Language

Toolkit) (4 or 5)– Final group project – Participation

• Office hours:– Barbara: Thursday 2:00-3:00 in Room 6 – Gopal: Tuesday 2:00-3:00 in Room 6 (to be confirmed)

Administrivia

• Communication:– My email: barbara.rosario@intel.com– Gopal : gopal.vaswani@gmail.com– Mailing list: i256@ischool.berkeley.edu

• Send an email to majordomo@ischool.berkeley.edu with subscribe i256 in the body

• Through intranet– Announcements: webpage and/or mailing list and/or Bspace

(TBA)– Public discussion: Bspace(?)

• Related course: Statistical Natural Language Processing, Spring 2009, CS 288 – http://www.cs.berkeley.edu/~klein/cs288/sp09/– Instructor: Dan Klein– Much more emphasis on statistical algorithms

• Questions?

Natural Language Processing

• Fundamental goal: deep understand of broad language– Not just string processing or keyword

matching!

• End systems that we want to build:– Ambitious: speech recognition, machine

translation, question answering…– Modest: spelling correction, text

categorization…

Slide taken from Klein’s course: UCB CS 288 spring 09

Example: Machine Translation

NLP applications

• Text Categorization– Classify documents by topics, language, author, spam filtering,

information retrieval (relevant, not relevant), sentiment classification (positive, negative)

• Spelling & Grammar Corrections• Information Extraction• Speech Recognition• Information Retrieval

– Synonym Generation

• Summarization• Machine Translation • Question Answering• Dialog Systems

– Language generation

Why NLP is difficult

• A NLP system needs to answer the question “who did what to whom”

• Language is ambiguous – At all levels: lexical, phrase, semantic– Iraqi Head Seeks Arms

• Word sense is ambiguous (head, arms) – Stolen Painting Found by Tree

• Thematic role is ambiguous: tree is agent or location?– Ban on Nude Dancing on Governor’s Desk

• Syntactic structure (attachment) is ambiguous: is the ban or the dancing on the desk?

– Hospitals Are Sued by 7 Foot Doctors• Semantics is ambiguous : what is 7 foot?

Why NLP is difficult

• Language is flexible– New words, new meanings – Different meanings in different contexts

• Language is subtle– He arrived at the lecture– He chuckled at the lecture– He chuckled his way through the lecture– **He arrived his way through the lecture

• Language is complex!

Why NLP is difficult

• MANY hidden variables– Knowledge about the world– Knowledge about the context– Knowledge about human communication techniques

• Can you tell me the time?

• Problem of scale– Many (infinite?) possible words, meanings, context

• Problem of sparsity – Very difficult to do statistical analysis, most things

(words, concepts) are never seen before

• Long range correlations

Why NLP is difficult

• Key problems:– Representation of meaning– Language presupposes knowledge about the

world– Language only reflects the surface of

meaning– Language presupposes communication

between people

Meaning

• What is meaning?– Physical referent in the real world– Semantic concepts, characterized also by relations.

• How do we represent and use meaning– I am Italian

• From lexical database (WordNet)• Italian =a native or inhabitant of Italy Italy = republic in southern

Europe [..]– I am Italian

• Who is “I”?– I know she is Italian/I think she is Italian

• How do we represent “I know” and “I think”• Does this mean that I is Italian? What does it say about the “I” and

about the person speaking?– I thought she was Italian

• How do we represent tenses?

Today

• Introductions• Administrivia• What is NLP• NLP Applications• Why is NLP difficult• Corpus-based statistical approaches• Course goals• What we’ll do in this course

Corpus-based statistical approaches to tackle NLP problem

– How can a can a machine understand these differences?

• Decorate the cake with the frosting• Decorate the cake with the kids

– Rules based approaches, i.e. hand coded syntactic constraints and preference rules:

• The verb decorate require an animate being as agent• The object cake is formed by any of the following, inanimate

entities (cream, dough, frosting…..)– Such approaches have been showed to be time

consuming to build, do not scale up well and are very brittle to new, unusual, metaphorical use of language

• To swallow requires an animate being as agent/subject and a physical object as object

– I swallowed his story– The supernova swallowed the planet

Corpus-based statistical approaches to tackle NLP problem

• A Statistical NLP approach seeks to solve these problems by automatically learning lexical and structural preferences from text collections (corpora)

• Statistical models are robust, generalize well and behave gracefully in the presence of errors and new data.

• So: – Get large text collections – Compute statistics over those collections– (The bigger the collections, the better the statistics)

Corpus-based statistical approaches to tackle NLP problem

• Decorate the cake with the frosting• Decorate the cake with the kids

• From (labeled) corpora we can learn that:#(kids are subject/agent of decorate) > #(frosting is subject/agent of

decorate)

• From (UN-labeled) corpora we can learn that:#(“the kids decorate the cake”) >> #(“the frosting decorates the cake”) #(“cake with frosting”) >> #(“cake with kids”)

etc..

• Given these “facts” we then need a statistical model for the attachment decision

Corpus-based statistical approaches to tackle NLP problem

• Topic categorization: classify the document into semantics topics

Document 1

The U.S. swept into the Davis Cup final on Saturday when twins Bob and Mike Bryan defeated Belarus's Max Mirnyi and Vladimir Voltchkov to give the Americans an unsurmountable 3-0 lead in the best-of-five semi-final tie.

Topic = sport

Document 2

One of the strangest, most relentless hurricane seasons on record reached new bizarre heights yesterday as the plodding approach of Hurricane Jeanne prompted evacuation orders for hundreds of thousands of Floridians and high wind warnings that stretched 350 miles from the swamp towns south of Miami to the historic city of St. Augustine.

Topic = disaster

Corpus-based statistical approaches to tackle NLP problem

• Topic categorization: classify the document into semantics topics

Document 1 (sport)

The U.S. swept into the Davis Cup final on Saturday when twins Bob and Mike Bryan …

Document 2 (disasters)

One of the strangest, most relentless hurricane seasons on record reached new bizarre heights yesterday as….

• From (labeled) corpora we can learn that:#(sport documents containing word Cup) > #(disaster documents

containing word Cup) -- feature

• We then need a statistical model for the topic assignment

Corpus-based statistical approaches to tackle NLP problem

• Feature extractions (usually linguistics motivated)

• Statistical models

• Data (corpora, labels, linguistic resources)

Goals of this Course

• Learn about the problems and possibilities of natural language analysis:– What are the major issues?– What are the major solutions?

• At the end you should:– Agree that language is difficult, interesting and important – Be able to assess language problems

• Know which solutions to apply when, and how• Feel some ownership over the algorithms

– Be able to use software to tackle some NLP language tasks– Know language resources– Be able to read papers in the field

What We’ll Do in this Course

• Linguistic Issues– What are the range of language phenomena?– What are the knowledge sources that let us

disambiguate?– What representations are appropriate?

• Applications

• Software (Python and NLTK)

• Statistical Modeling Methods

What We’ll Do in this Course

• Read books, research papers and tutorials• Final project

– Your own ideas or chose from some suggestions I will provide

– We’ll talk later during the couse about ideas/methods etc. but come talk to me if you have already some ideas

• Learn Python• Learn/use NLTK (Natural Language ToolKit) to

try out various algorithms

Python - Simple yet powerful

The zen of python : http://www.python.org/dev/peps/pep-0020/

• Very clear, readable syntax• Strong introspection capabilities

– http://www.ibm.com/developerworks/linux/library/l-pyint.html (recommended) 

• Intuitive object orientation• Natural expression of procedural code• Full modularity, supporting hierarchical packages• Exception-based error handling• Very high level dynamic data types• Extensive standard libraries and third party modules for virtually every task

– Excellent functionality for processing linguistic data.– NLTK is one such extensive third party module. 

Source : python.org

Python

Source : nltk.org

Language processing task NLTK modules Functionality

Accessing corpora nltk.corpus standardized interfaces to corpora and lexicons

String processing nltk.tokenize, nltk.stem tokenizers, sentence tokenizers, stemmers

Collocation discovery nltk.collocations t-test, chi-squared, point-wise mutual information

Part-of-speech tagging nltk.tag n-gram, backoff, Brill, HMM, TnT

Classification nltk.classify, nltk.cluster decision tree, maximum entropy, naive Bayes, EM, k-means

Chunking nltk.chunk regular expression, n-gram, named-entity

Parsing nltk.parse chart, feature-based, unification, probabilistic, dependency

This is not the complete list

NLTK• NLTK defines an infrastructure that can be used to build NLP programs in Python.• It provides basic classes for representing data relevant to natural language

processing. • Standard interfaces for performing tasks such as part-of-speech tagging, syntactic

parsing, and text classification. • Standard implementations for each task which can be combined to solve complex

problems.

Resources:• Download at http://www.nltk.org/download• Getting started with NLTK Chapter 1 • NLP and NLTK talk at google http://www.youtube.com/watch?v=keXW_5-llD0

Topics

• Text corpora & other resources• Words (Morphology, tokenization, stemming, part-of-

speech, WSD, collocations, lexical acquisition, language models)

• Syntax: chunking, PCFG & parsing • Statistical models (esp. for classification) • Applications

– Text classification– Information extraction– Machine translation– Semantic Interpretation– Sentiment Analysis – QA / Summarization– Information retrieval

Next Assignment• Due before next class Tue Sep 1

– No turn-in• Download and install Python and NLTK• Download the NLTK Book Collection, as described at

the beginning of chapter 1 of the book Natural Language Processing with Python

• Readings:– Chapter 1 of the book Natural Language Processing

with Python – Chapter 3 of Foundations of Statistical NLP

• Next class:– Linguistic Essentials– Python Introduction