I256 Applied Natural Language Processing Fall 2009

1. I256 Applied Natural Language ProcessingFall 2009 Lecture 1 Introduction Barbara Rosario

2. Introductions • Barbara Rosario • iSchool alumni (class 2005) • Intel Labs • Gopal Vaswani • iSchool master student (class 2010) • You?

3. 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

4. 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)

5. 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?

6. 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

7. Example: Machine Translation

8. 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

9. 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?

10. 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!

11. 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

12. 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

13. 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?

14. 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

15. 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

16. 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)

17. 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: #(“thekids decorate the cake”) >> #(“thefrosting decorates the cake”) #(“cake with frosting”) >> #(“cake with kids”) etc.. • Given these “facts” we then need a statistical model for the attachment decision

18. Corpus-based statistical approaches to tackle NLP problem • Topic categorization: classify the document into semantics topics

19. Corpus-based statistical approaches to tackle NLP problem • Topic categorization: classify the document into semantics topics • 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

20. Corpus-based statistical approaches to tackle NLP problem • Feature extractions (usually linguistics motivated) • Statistical models • Data (corpora, labels, linguistic resources)

21. 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

22. 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

23. 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

24. Python - Simple yet powerfulThe 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.  Python Source : python.org

25. 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 This is not the complete list Source : nltk.org

26. 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

27. 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