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Language and Knowledge Technologies for News Collections in Croatia

Language and Knowledge Technologies for News Collections in Croatia. Bojana Dalbelo Bašić, Marko Tadić University of Zagreb, Faculty of Electrical Engineering and Computing / Faculty of Humanities and Social Sciences bojana . dalbelo @ fe r . hr, marko.tadic @ ffzg.hr

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Language and Knowledge Technologies for News Collections in Croatia

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  1. Language and Knowledge Technologies for News Collections in Croatia Bojana Dalbelo Bašić, Marko Tadić University of Zagreb,Faculty of Electrical Engineering and Computing / Faculty of Humanities and Social Sciences bojana.dalbelo@fer.hr, marko.tadic@ffzg.hr ITN2008Dubrovnik2008-05-21 ITN2008Dubrovnik 2008-05-21

  2. Talk overview • who we are? • what are we doing? • text collections used for research • applicable language technologies • applicable knowledge technologies ITN2008Dubrovnik 2008-05-21

  3. Who we are? • University of Zagreb, Croatia • two faculties in a joint mission • build the systems that will develop and enable the usage of language resources and tools for Croatian ITN2008Dubrovnik 2008-05-21

  4. Who we are 2? • Faculty of Humanities andSocial Sciences • Institute/Department ofLinguistics • Department of InformationSciences • basic computational linguistic tasks for Croatian • compiling and processing large language resources • Croatian National Corpus, Croatian Morphological Lexicon, Croatian WordNet, Croatian Dependency Treebank • digitalization of Croatian lexicographic heritage:60+ dictionaries digitalized so far • tagger, lemmatizer • chunker, parser • NERC system, gazeteers (e.g. Croatian (sur)names) ITN2008Dubrovnik 2008-05-21

  5. Who we are 3? • Faculty of Electrical Engineering and Computing • Department of Electronics, Microelectronics, Computer and Intelligent Systems / KTLab • Knowledge Technogies Laboratory Group deals with • text preprocessing techniques for Croatian for machine learning procedures • dimensionality reduction and document clustering in the vector space model + visualisation • automatic indexing ofdocuments • intelligent, language specificand non-specific informationretrieval and extraction ITN2008Dubrovnik 2008-05-21

  6. What are we doing? • working jointly on several research projects • AIDE: Automatic Indexing with Descriptors from Eurovoc (cooperation with the Government of the Republic of Croatia, HIDRA) • Institute of Linguistics/FFZG & ZEMRIS/FER, 2006-2008 • Computational Linguistic Models and Language Technologies for Croatian (rmjt.ffzg.hr), 2007-2011 • national research programme, prof. Marko Tadić • Sources for Croatian Heritage and Croatian European Identity, 2007-2011 • national research programme, prof. Damir Boras • CADIAL: Computer Aided Document Indexing for Accessing Legislation • joint Flemish-Croatian project, 2007-2009 • prof. Marie-Francine Moens & prof. Bojana Dalbelo Bašić ITN2008Dubrovnik 2008-05-21

  7. What are we doing 2? • Composition of the programme RMJT • P1: Croatian language resources and their annotation • project leader: Marko Tadić • P2: Computational syntax of Croatian • project leader: Zdravko Dovedan • P3: Lexical semantics in building Croatian WordNet • project leader: Ida Raffaelli • P4: Information technology in translating Croatian and language e-learning • project leader: Sanja Seljan • P5: Knowledge discovery in textual data • project leader: Bojana Dalbelo Bašić • participation in a FP7 project CLARIN • LR & LT as a research infrastructure for e-SSH ITN2008Dubrovnik 2008-05-21

  8. Text collections used for research • we have done research on different kinds of texts, but predominantly in journalistic genre • Croatian National Corpus (hnk.ffzg.hr) • 101,2 million tokens in size • newspaper articles: 37% (ca 37 million tokens) • magazines articles: 16% (ca 16 million tokens) • Croatian-English Parallel Corpus • 3,5 million tokens from Croatian Weekly • newspaper articles: 100%, bilingual • special text collections • database of Vjesnik articles: 2000-2003, >90,000 articles • Narodne novine collection: 1998-2008, >10,000 texts, >15 million tokens • Parallel corpus of Southeast European Times: 2007-, >25,000 articles, >4 million tokens, in 10 languages ITN2008Dubrovnik 2008-05-21

  9. Applicable language technologies • morphological processing • important for inflectionally rich languages, e.g. • Croatian noun in 14 word-forms (7 cases, 2 numbers): N: student studenti G: studenta studenata D: studentu studentima A: studenta studente V: studentu studenti L: studentu studentima I: studentom studentima • unlike English noun in 2(4?) word-forms (2 numbers+ possesive?): Sg: student Poss: (student’s) Pl: students Poss: (students’) • present in all Slavic languages (excl. Bulgarian), German, Greek, Baltic languages, Finnish, ... ITN2008Dubrovnik 2008-05-21

  10. Applicable language technologies 2 • recognizing to which lexeme(s) a WF belongs to • helps us in avoiding the problem of data sparsness in many text processing tasks: • information retrieval • text mining • document classification • document indexing • query processing • search engines are not “inflectionally sensitive” • speakers of inflectionally rich language use the normal/base form = lemma • e.g. www.google.hr input: noun in nominative singular • did you know that accusative and genitive are more frequent in Croatian? ITN2008Dubrovnik 2008-05-21

  11. Applicable language technologies 3 ITN2008Dubrovnik 2008-05-21

  12. Applicable language technologies 4 ITN2008Dubrovnik 2008-05-21

  13. Applicable language technologies 5 ITN2008Dubrovnik 2008-05-21

  14. Applicable language technologies 6 • Named Entity Recognition and Classification (NERC) • NEs are introducing the exact information from outer world into the world-of-text • represent answers to the basic journalistic questions: who?, where?, when?, how much? • types of NEs (according to MUC conferences) • person • organization • location • date • time • valute and measurements • percentage • system that works for Croatian with >90% precision ITN2008Dubrovnik 2008-05-21

  15. Applicable language technologies 7 • system that works for Croatian with >90% precision ITN2008Dubrovnik 2008-05-21

  16. Applicable language technologies 8 • semantic networks as language resources • covering the general lexicon and NEs in a language • WordNet: words are linked by meaning • synonyms, antonyms, hypo-/hyperonyms, meronyms… • realized as ontologies or taxonomies • allow for words and/or NEs • synonymy/antonymy search • evoking upper-levels in taxonomy • e.g. activating the region/state/continent when a city is mentioned or a company when a director is in focus • explicit social networking connections between NEs ITN2008Dubrovnik 2008-05-21

  17. Applicable L&K technologies ITN2008Dubrovnik 2008-05-21

  18. Applicable L&K technologies ITN2008Dubrovnik 2008-05-21

  19. Applicable language technologies 8 • semantic networks as language resources • covering the general lexicon and NEs in a language • WordNet: words are linked by meaning • synonyms, antonyms, hypo-/hyperonyms, meronyms… • realized as ontologies or taxonomies • allow for words and/or NEs • synonymy/antonymy search • evoking upper-levels in taxonomy • e.g. activating the region/state/continent when a city is mentioned or a company when a director is in focus • explicit social networking connections between NEs • semantic processing: roles in sentences (agent, patient, instrument etc.) ITN2008Dubrovnik 2008-05-21

  20. Applicable language technologies 8 • semantic networks as language resources • covering the general lexicon and NEs in a language • WordNet: words are linked by meaning • synonyms, antonyms, hypo-/hyperonyms, meronyms… • realized as ontologies or taxonomies • allow for words and/or NEs • synonymy/antonymy search • evoking upper-levels in taxonomy • e.g. activating the region/state/continent when a city is mentioned or a company when a director is in focus • explicit social networking connections between NEs • semantic processing: roles in sentences (agent, patient, instrument etc.) • event detection: from verbal frames and scenarios ITN2008Dubrovnik 2008-05-21

  21. Applicable language technologies 8 • semantic networks as language resources • covering the general lexicon and NEs in a language • WordNet: words are linked by meaning • synonyms, antonyms, hypo-/hyperonyms, meronyms… • realized as ontologies or taxonomies • allow for words and/or NEs • synonymy/antonymy search • evoking upper-levels in taxonomy • e.g. activating the region/state/continent when a city is mentioned or a company when a director is in focus • explicit social networking connections between NEs • semantic processing: roles in sentences (agent, patient, instrument etc.) • event detection: from verbal frames and scenarios • connection with geo-data ITN2008Dubrovnik 2008-05-21

  22. Applicable knowledge technologies • automatic document indexing • eCADIS system • developed for Croatian legal docs • applicable to any document collection • uses machine learning techniques • automatically attaches the keywords (descriptors) from a controlled thesaurus to a document • represent the document content description • integrates the corpus and document analysis ITN2008Dubrovnik 2008-05-21

  23. CADIS system ITN2008Dubrovnik 2008-05-21

  24. ITN2008Dubrovnik 2008-05-21

  25. eCADIS system • integrates the information from the whole document collection • greyed n-grams are statistically relevant in the corpus i.e. collocations ITN2008Dubrovnik 2008-05-21

  26. eCADIS system • automatic suggestion of relevant descriptors, hence the automatic indexing ITN2008Dubrovnik 2008-05-21

  27. eCADIS system • compare it to manually attached descriptors… ITN2008Dubrovnik 2008-05-21

  28. Applicable knowledge technologies • automatic document classification • uses a series of classifiers, combined 3500 classifiers • results represented in a vector-space model • dimensionality reduction • matrices could be huge (Vjesnik: 90,000 x 600,000) • features selected • types • lemmas • collocations • NEs • … • evaluated by F1 measure (combination of precision/recall) • F1 > 90% in most of cases ITN2008Dubrovnik 2008-05-21

  29. Applicable knowledge technologies • visualisationof classification between pages • Croatia Weekly • English side • go= economyks = culture/sportte = turism/ecol.po = politics ITN2008Dubrovnik 2008-05-21

  30. Applicable knowledge technologies • visualisationof classification between culture (low right) and sport (high left) • Croatia Weekly • English side • go= economyks = culture/sportte = turism/ecol.po = politics ITN2008Dubrovnik 2008-05-21

  31. Applicable knowledge technologies • visualisationof classification for documents that differentiate between home (blue upward) and foreign policy (blue downward) • Croatia Weekly • English side • go= economyks = culture/sportte = turism/eco.po = politics ITN2008Dubrovnik 2008-05-21

  32. Language and Knowledge Technologies for News Collections in Croatia Bojana Dalbelo Bašić, Marko Tadić University of Zagreb,Faculty of Electrical Engineering and Computing / Faculty of Humanities and Social Sciences bojana.dalbelo@fer.hr, marko.tadic@ffzg.hr ITN2008Dubrovnik2008-05-21 ITN2008Dubrovnik 2008-05-21

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