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COMP 791A: Statistical Language Processing

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COMP 791A: Statistical Language Processing Introduction Chap. 1 Course information Prof: Leila Kosseim Office: LB 903-7 Email: kosseim_at_cs.concordia.ca Office hours ... – PowerPoint PPT presentation

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Title: COMP 791A: Statistical Language Processing


1
COMP 791A Statistical Language Processing
Introduction Chap. 1
2
Course information
  • Prof Leila Kosseim
  • Office LB 903-7
  • Email kosseim_at_cs.concordia.ca
  • Office hours TBA

3
Goal of NLP
  • Develop techniques and tools to build practical
    and robust systems that can communicate with
    users in one or more natural language

Natural Lang. Artificial Lang.
Lexical gt100 000 words 100 words
Syntax Complex Simple
Semantic 1 word --gt several meanings 1 word --gt 1 meaning
4
References
  • Foundations of Statistical Natural Language
    Processing, by Chris Manning and Hinrich Schutze,
    MIT Press, 1999.
  • Speech and Language Processing, Daniel Jurafsky
    James H. Martin. Prentice Hall, 2000. 
  • Current literature available on the Web.
  • See course Web page www.cs.concordia.ca/kosseim/
    Teaching/COMP791-W04/

5
Other References
  • Proceedings of major conferences
  • ACL Association for Computational Linguistics
  • EACL European chapter of ACL
  • ANLP Applied NLP
  • COLING Computational Linguistics
  • TREC Text Retrieval Conference

6
Who studies languages?
  • Linguist
  • What constraints the possible meanings of a
    sentence?
  • Uses mathematical models (ex. formal grammars)
  • Psycholinguist
  • How do people produce a discourse from an idea?
  • Uses experimental observations with human
    subjects
  • Philosopher
  • What is meaning anyways?
  • How do words identify objects in the world?
  • Uses argumentations, examples and
    counter-examples
  • Computational Linguist (NLP)
  • How can we identify the structure of sentences
    automatically?
  • Uses data structures, algorithms, AI techniques
    (search, knowledge-representation, machine
    learning, )

7
Why study NLP?
  • necessary to many useful applications
  • information retrieval,
  • information extraction,
  • filtering,
  • spelling and grammar checking,
  • automatic text summarization,
  • understanding and generation of natural language,
  • machine translation

8
Who needs NLP?
  • Too many texts to manipulate
  • On Internet
  • E-mails
  • Various corporate documentation
  • Too many languages
  • 39000 languages and dialects

9
Languages on the Internet
Source Global Reach (www.glreach.com)
10
Source Global Reach (www.glreach.com)
11
Applications of NLP
  • Text-based processing of written texts (ex.
    Newspaper articles, e-mails, Web pages)
  • Text understanding/analysis (NLU)
  • IR, IE, MT,
  • Text generation (NLG)
  • Dialog-based systems (human-machine
    communication)
  • Ex QA, tutoring systems,

12
Brief history of NLP
  • 1940s - 1950s Foundational Insights
  • Automata, finite-state machines formal
    languages (Turing, Chomsky, BackusNaur)
  • Probability and information theory (Shannon)
  • Noisy channel and decoding (Shannon)
  • 1960s - 1970s Two Camps
  • Symbolic Linguists Computer Scientists
  • Transformational grammars (Chomsky, Harris)
  • Artificial Intelligence (Minsky, McCarthy)
  • Theorem Proving, heuristics, general problem
    solver (NewellSimon)
  • Stochastic Statisticians Electrical Engineers
  • Bayesian reasoning for character recognition
  • Authorship attribution
  • Corpus Work

13
Brief history of NLP (cont)
  • 1970s - 1980s 4 Paradigms
  • Stochastic approaches
  • Logic-based / Rule-based approaches
  • Scripts and plans for NL understanding of toy
    worlds
  • Discourse modeling (discourse structures
    coreference resolution)
  • Late 1980s - 1990s Rise of probabilistic models
  • Data-driven probabilistic approaches (more
    robust)
  • Engineering practical solutions using automatic
    learning
  • Strict evaluation of work

14
Why study NLP Statistically?
  • Up to about 10 years, NLP was mainly investigated
    using a rule-based approach.
  • But
  • Rules are often too strict to characterize
    peoples use of language (people tend to stretch
    and bend rules in order to meet their
    communicative needs.)
  • Need (expert) people to develop rules (knowledge
    acquisition bottleneck)
  • Statistical methods are more flexible more
    robust

15
Tools and Resources Needed
  • Probability/Statistical Theory
  • Statistical Distributions, Bayesian Decision
    Theory.
  • Linguistics Knowledge
  • Morphology, Syntax, Semantics, Pragmatics
  • Corpora
  • Bodies of marked or unmarked text
  • to which statistical methods and current
    linguistic knowledge can be applied
  • in order to discover novel linguistic theories or
    interesting and useful knowledge to build
    applications.

16
The Alphabet Soup
  • NLP ? Natural Language Processing
  • CL ? Computational Linguistics
  • NLE ? Natural Language Engineering
  • HLT ? Human Language Technology
  • IE ? Information Extraction
  • IR ? Information Retrieval
  • MT ? Machine Translation
  • QA ? Question-Answering
  • POS ? Part-of-speech
  • NLG ? Natural Language Generation
  • NLU ? Natural Language Understanding

17
Why is NLP difficult?
  • Because Natural Language is highly ambiguous.
  • Syntactic ambiguity
  • I made her duck.
  • has 2 parses (i.e., syntactic analysis)
  • The president spoke to the nation about the
    problem of drug use in the schools from one coast
    to the other.
  • has 720 parses.
  • Ex
  • to the other can attach to any of the previous
    NPs (ex. the problem), or the head verb ? 6
    places
  • from one coast has 5 places to attach

(S (NP I) (VP (V made) (NP (PRO her) (N duck))) (S (NP I) (VP (V made) (NP (PRO her) (VP (V duck))))
18
Why is NLP difficult? (cont)
  • Word category ambiguity
  • book --gt verb? or noun?
  • Word sense ambiguity
  • bank --gt financial institution? building? or
    river side?
  • Words can mean more than their sum of parts
  • make up a story
  • Fictitious worlds
  • People on mars can fly.
  • Defining scope
  • People like ice-cream.
  • Does this mean that all (or some?) people like
    ice cream?
  • Language is changing and evolving
  • Ill email you my answer.
  • This new S.U.V. as a compartment for your mobile
    phone.

19
Methods that do not work well
  • Hand-coded rules
  • produce a knowledge acquisition bottleneck
  • perform poorly on naturally occurring text
  • Ex Hand-coded syntactic constraints and
    preference rules
  • Ex selectional restrictions
  • animate being --gt swallow--gt physical
    object
  • I swallowed his story / line.
  • The supernova swallowed the planet.

20
What Statistical NLP can do
  • seeks to solve the acquisition bottelneck
  • by automatically learning preferences from
    corpora (ex, lexical or syntactic preferences).
  • offers a solution to the problem of ambiguity and
    "real" data because statistical models
  • are robust
  • generalize well
  • behave gracefully in the presence of errors and
    new data.

21
Some standard corpora
  • Brown corpus
  • 1 million words
  • Tagged corpus (POS)
  • Balanced (representative sample of American
    English in the 1960-1970) (different genres)
  • Lancaster-Oslo-Bergen (LOB) corpus
  • British replication of the Brown corpus
  • Susanne corpus
  • Free subset of Brown corpus (130 000 words)
  • Syntactic structure
  • Penn Treebank
  • Syntactic structure
  • Articles from Wall Street Journal
  • Canadian Hansard
  • Bilingual corpus of parallel texts

22
What to do with text corpora? Count words
  • Count words to find
  • What are the most common words in the text?
  • How many words are in the text?
  • word tokens vs word types
  • What is the average frequency of each word in
    the text?

23
Whats a word anyways?
  • I have a can opener but I cant open these cans.
  • how many words?
  • Word form
  • inflected form as it appears in the text
  • can and cans ... different word forms
  • Lemma
  • a set of lexical forms having the same stem, same
    POS and same meaning
  • can and cans same lemma
  • Word token
  • an occurrence of a word
  • I have a can opener but I cant open these cans.
    11 word tokens (not counting punctuation)
  • Word type
  • a different realization of a word
  • I have a can opener but I cant open these cans.
    10 word types (not counting punctuation)

24
An example
  • Mark Twains Tom Sawyer
  • 71,370 word tokens
  • 8,018 word types
  • tokens/type ratio 8.9 (indication of text
    complexity)
  • Complete Shakespeare work
  • 884,647 word tokens
  • 29,066 word types
  • tokens/type ratio 30.4

25
Common words in Tom Sawyer
  • but words in NL have an uneven distribution

26
Frequency of frequencies
  • most words are rare
  • 3993 (50) word types appear only once
  • they are called happax legomena (read only once)
  • but common words are very common
  • 100 words account for 51 of all tokens (of all
    text)

27
Word counts are interesting...
  • As an indication of a texts style
  • As an indication of a texts author
  • But, because most words appear very infrequently,
  • it is hard to predict much about the behavior of
    words (if they do not occur often in a corpus)
  • --gt Zipfs Law

28
Zipfs Law
  • Count the frequency of each word type in a large
    corpus
  • List the word types in order of their frequency
  • Let
  • f frequency of a word type
  • r its rank in the list
  • Zipfs Law says f ? 1/r
  • In other words
  • there exists a constant k such that f r k
  • The 50th most common word should occur with 3
    times the frequency of the 150th most common
    word.

29
Zipfs Law on Tom Saywer
  • k 8000-9000
  • except for
  • The 3 most frequent words
  • Words of frequency 100

30
Plot of Zipfs Law
  • On chap. 1-3 of Tom Sawyer (? numbers from p.
    2526)
  • fr k

31
Plot of Zipfs Law (cont)
  • On chap. 1-3 of Tom Sawyer
  • fr k gt log(fr) log(k) gt log(f)log(r)
    log(k)

32
Zipfs Law, so what?
  • There are
  • A few very common words
  • A medium number of medium frequency words
  • A large number of infrequent words
  • Principle of Least effort Tradeoff between
    speaker and hearers effort
  • Speaker communicates with a small vocabulary of
    common words (less effort)
  • Hearer disambiguates messages through a large
    vocabulary of rare words (less effort)
  • Significance of Zipfs Law for us
  • For most words, our data about their use will be
    very sparse
  • Only for a few words will we have a lot of
    examples

33
Another Zipf law on language
  • Nb of meanings of a word is correlated to its
    frequency
  • the more frequent a word, the more senses it can
    have
  • Ex
  • Words at rank 2,000 have 4.6 meanings
  • Words at rank 5,000 have 3 meanings
  • Words at rank 10,000 have 2.1 meanings
  • Ex Verb senses in WordNet
  • serve has 13 senses
  • but most verbs have only 1 sense

f frequency of word m num of senses r rank
of word
34
Yet another Zipf law on language
  • Content words tend to "clump" together
  • if we take a text and count the distance between
    identical words (tokens)
  • then the freq of intervals of size s between
    identical tokens is inversely proportional to the
    size s
  • i.e. we have a large number of small intervals
  • i.e. we have a small number of large intervals
  • --gt most content words occur near each other

f frequency of intervals of size s s size of
interval p varied between 1 and 1.3
xxx
xxx
xxx
xxx
35
What to do with text corpora? Find Collocations
  • Collocation a phrase where the whole expression
    is perceived as having an existence beyond the
    sum of its parts
  • disk drive, make up, bacon and eggs
  • important for machine translation
  • strong tea --gt thé fort
  • strong argument --gt?argument fort (convainquant)
  • can be extracted from a text
  • find the most common bigrams
  • however, since these bigrams are often
    insignificant (ex, at the, of a)
  • they can be filtered.

36
Collocations
Raw bigrams
Filtered bigrams
37
What to do with text corpora? Concordances
  • Find the different contexts in which a word
    occurs.
  • Key Word In Context (KWIC) concordancing program.

38
Concordances
  • useful for
  • Finding syntactic frames of verbs
  • Transitive? Intransitive?
  • Building dictionaries for learners of foreign
    languages
  • Guiding statistical parsers
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