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Lec 1


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computational linguistics Ferdowsi university

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Lec 1

  1. 1. Computational LinguisticsLecture 1: Introduction What is Linguistics? Prescriptive Grammar: Descriptive Grammar: Some Linguistic Methods Grammatical Theory Linguistics Beyond Grammar linguistics What is computational linguistics? Dr. Saeed Rahati
  2. 2. What is Linguistics? Linguistics is the study of human language, broadly construed. Linguistics is a scientific discipline with established theories, analytic methods, and real-world applications. Linguists often study individual languages, but... When linguists study individual languages, they have larger issues in mind.
  3. 3. … What is Linguistics? 3Linguistics is descriptive,not prescriptive
  4. 4. Prescriptive Grammar: 4 Rules against certain usages. Few if any rules for what is allowed. Condemns forms generally in use. Explicitly normative enterprise.
  5. 5. Descriptive Grammar: Rules characterizing what people do say. Tries to do so in a way that reflects internalized generalizations that people have made. Linguists are fundamentally concerned with linguistic knowledge.
  6. 6. Anyway, language isn’t logical: 6• parkway vs. driveway• maternity dress vs. paternity suit• bathing trunks (pl) vs. bikini (sing)• you are vs. *you is• Aren’t I clever? vs. *I aren’t clever.
  7. 7. Some Linguistic Methods 7◮ Fieldwork◮ Formal analysis of patterns in datasets◮ Psycholinguistic experiments◮ Computational modeling◮ Corpus analysis
  8. 8. Grammatical Theory 8◮ Phonetics: The study of speech sounds◮ Phonology: The study of sound systems◮ Morphology: The study of word structure◮ Syntax: The study of sentence structure◮ Semantics: The study of linguistic meaning◮ Pragmatics: The study of language use
  9. 9. Phonetics: The Study of SpeechSounds 9
  10. 10. Phonology: The Study of SoundSystems 10
  11. 11. Morphology: The Study Of Word Structure 11◮ missile: ‘ICBM’◮ anti-tank-missile: ‘missile targettingtanks’◮ anti-aircraft-missile: ‘missile targettingaircraft’◮ anti-missile-missile: ‘missile targettingICBMs’
  12. 12. Morphological Rules 12◮ Rule: Anti-X-missile is a missiletargetting Xs.◮ What kind of missile targets anti-missile-missiles?◮ anti-anti-missile-missile-missile◮ anti-anti-anti-missile-missile-missile-missile:‘missile targetting anti-anti-missile-missile-missiles’
  13. 13. Syntax: The Study of Sentence Structure 13◮ I saw the woman with the telescope.I [saw [the woman] [with the telescope]].I [saw [[the woman] [with the telescope]]].◮ Put the block in the box on the tablein the bedroom.◮ Put the block in the box on the tablein the bedroom near the kitchen.
  14. 14. Semantics: The Study of Linguistic Meaning 14◮ Structural Ambiguity produces semanticambiguity.◮ Both in morphology and syntax.◮ Lexical Ambiguity: We screened thecandidates.◮ Both Together: I saw her duck.
  15. 15. Pragmatics: The Study of Language Use 15Q: Is Palin a Republican?A: Is the Pope Catholic?◮ Why don’t you move up to the City?◮ Why should I stand here and listento this?◮ Do you think I’m saying this just tohear the sound of my own voice?
  16. 16. Linguistics Beyond Grammar 16◮ Historical Linguistics: How languages change overtime.◮ Sociolinguistics: How languages vary socially. Howlanguage is used as a social resource.◮ Psycholinguistics: What goes on in people’s headsas they use language.◮ Language Acquisition: How people learn language.(first language acquisition; second languageacquisition)◮ Computational Linguistics: Making computersprocess (generate/‘understand’/translate...) humanlanguages.
  17. 17. Computational Linguistics 17 computational linguistics linguistics? chemistry bioloneuropsychologypsychology literaryphysics gy criticism more rigorous flakey more less rigorous
  18. 18. What defines the rigor of afield? 18 Whether results are reproducible Whether theories are testable/falsifiable Whether there are a common set of methods for similar problems Whether approaches to problems can yield interesting new questions/answers
  19. 19. Linguistics 19 Computational
  20. 20. Linguistics 20 engineering linguistics sociology literary criticism more rigorous less rigorous Computational
  21. 21. other areas of less sociolinguistics rigorous (e.g. Deborah Tannen) ( “theoretical” linguistics (e.g. minimalist s syntax)The true situation with “theoretical” linguistics (e.g. lexical-functional g grammar) historical linguistics some areas oflinguistics sociolinguistics ( (e.g. Bill Labov) psycholinguistics experimental phonetics more rigorous
  22. 22. What is computationallinguistics? 22 Text normalization/segmentation Morphological analysis Automatic word pronunciation prediction Transliteration Word-class prediction: e.g. part of speech tagging Parsing Semantic role labeling Machine translation Dialog systems Topic detection Summarization Text retrieval Bioinformatics Language modeling for automatic speech recognition C Computer-aided language learning (CALL)
  23. 23. Computational linguistics 23 Often thought of as natural language engineering But there is also a serious scientific component to it.
  24. 24. Goals of Computational Linguistics/Natural Language Processing 24 To get computers to deal with language the way humans do:  They should be able to understand language and respond appropriately in language  They should be able to learn human language the way children do  They should be able to perform linguistic tasks that skilled humans can do, such as translation Yeah, right
  25. 25. Some interesting themes… 25 Finite-state methods:  Many application areas  Raises many interesting questions about how “regular” language is Grammar induction:  Linguists have done a poor job at their stated goal of explaining how humans learn grammar Computational models of language change:  Historical evidence for language change is only partial. There are many changes in language for which we have no direct evidence.
  26. 26. Why CL may seem ad hoc 26 Wide variety of areas (as in linguistics) If it’s natural language engineering the goal is often just to build something that works Techniques tend to change in somewhat faddish ways…  For example: machine learning approaches fall in and out of favor