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The Rôle of Linguistics for the Future of Language Processing

The Rôle of Linguistics for the Future of Language Processing. Hans Uszkoreit German Research Center for Artificial Intelligence and Saarland University at Saarbruecken. Outline. The development of linguistics Linguistics and the computer The relevance of CL for theoretical linguistics

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The Rôle of Linguistics for the Future of Language Processing

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  1. The Rôle of Linguisticsfor the Future of Language Processing • Hans Uszkoreit • German Research Center for Artificial Intelligence • and Saarland University at Saarbruecken

  2. Outline • The development of linguistics • Linguistics and the computer • The relevance of CL for theoretical linguistics • The role of linguistics for language technology • Current trends and outlook

  3. IT in Science • Data-Gathering and Maintenance • automatic handling of large volumes of data • Scientific Computing • data and model visualization • data exploitation, • simulation • modelling • Electronic scientific information • data on research (centers, people, resources, projects, literature) • Electronic scientific content • reports, articles, books, e-journals, e-print archives

  4. Development of Linguistics • first half of 20th century: linguistics becomes concrete structuralist linguistics - ontological concepts (entities and structures) • second half of 20th century: linguistics becomes formalgenerative linguistics - formalisms for syntax and semantics • first half of 21st century: linguistics becomes empirical empirical linguistics - quantitative models - graded grammaticality

  5. The Rôle of Computation • formalization led to highly complex systems of formal rules, principles or constraints that cannot be tested, validated and modified without sophisticated information processing • language data of sufficient size cannot be gathered, searched, and maintained anymore without powerful computing

  6. Empirical Linguistics • discrete findings • statistical findings • replicability • shared interpretations of data • connection with data and results

  7. EMPIRICAL LINGUISTICS introspective data research experimental psycholinguistic data corpus data DB of relevant data

  8. Driving Forces of CL Linguistics linguistic theory Engineering language technology applications Cognition models of human language processing

  9. theoretical linguistics applied linguistics linguistics w/o the computer linguistics with the computer Role of Computing in Linguistics

  10. Until 1980 Linguistics Computational Linguistics

  11. 1980-1990 Linguistics Computational Linguistics

  12. 1990 - 2000 Linguistics Computational Linguistics

  13. LTMETHODS non-discrete discrete hybrid shallow HMM-based POS Tagger deep

  14. LTMETHODS non-discrete discrete hybrid shallow HPSG-Parser with MRS deep

  15. LTMETHODS non-discrete discrete hybrid shallow PCF Parser deep

  16. LTMETHODS non-discrete discrete hybrid shallow syntactic LFG parser with ME selection deep

  17. LTMETHODS (Trends) non-discrete discrete hybrid shallow deep

  18. S VP NP NP V NP Det N A N Sue gave Paul an old penny. Simulation and Modelling

  19. S S S/NP VP NP NP NP NP V NP Det N V NP Det N A N A N Sue gab Paul einen alten Pfennig. Sue gave Paul an old penny. $x[(old'(penny')) (x) Ù (Past(give'(sue‘, paul‘, x)))]

  20. APPLICATIONS • Machine Translation e.g. Systran, Logos, METAL-Comprendium, IBM PT • Access to Databasese.g. Core Language Engine • New: Information Extraction and Text Enrichmente.g. WHITEBOARD, DEEP THOUGH

  21. Problems with Deep Analysis • Coverage (Development Time) • Robustness (Coping with Out-of-Grammar Input) • Efficiency (Runtime and Space Efficiency) • Specificity (Selection among Readings)

  22. Outlook • Linguistics will develop hybrid discrete and nondiscrete models of language • More subareas of linguistics will employ computational modelling • Computational linguistics will play a central role in the emprirical branch of linguistic research • Computational linguistics methods and results do have a future in language technology • Language technology will have to get more deeply into semantics • The field provides some grand challenges

  23. Grand Challenges • hybrid models of language processing and learning, • models of language change • empirical methodology of language science: large multilevel linguistically interpreted data collections • ambient computing -- ubiquitous natural access to information and assistance • turning the WWW as well as personal and collective digital infor-mation repositories into digital memories and knowledge bases

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