Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал: http://ena.lp.edu.ua:8080/handle/ntb/39416
Повний запис метаданих
Поле DCЗначенняМова
dc.contributor.authorDavydov, M.-
dc.contributor.authorLozynska, O.-
dc.contributor.authorPasichnyk, V.-
dc.date.accessioned2018-02-12T13:09:14Z-
dc.date.available2018-02-12T13:09:14Z-
dc.date.issued2017-
dc.identifier.citationDavydov M. Partial semantic parsing of sentences by means of grammatically augmented ontology and weighted affix context-free grammar / M. Davydov, O. Lozynska, V. Pasichnyk // Econtechmod : an international quarterly journal on economics in technology, new technologies and modelling processes. – Lublin ; Rzeszow, 2017. – Volum 6, number 2. – P. 27–32. – Bibliography: 24 titles.uk_UA
dc.identifier.urihttp://ena.lp.edu.ua:8080/handle/ntb/39416-
dc.description.abstractIn spite of the fact that modern statistical and neural net based tools for parsing natural language texts supersede classical approaches there are still areas where generative grammars are used. These are areas where collection of universal parallel corpuses is still in the progress. National sign languages are among them. Ontologies and common sense databases play valuable role in parsing and translation of such languages. Grammatically augmented ontology (GAO) is an ontology extension that links phrases to their meaning. The link is established via special expressions that connect phrase meaning to grammatical and semantical attributes of words that constitute it. The article introduces a new approach to sentence parsing that is based on integration of ontology relations into productions of weighted affix context-free grammar (WACFG). For that reason a new parser for WACFG grammar was developed inspired by works of C.H.A. Koster. Basic properties of WACFG are discussed and the algorithm for selection and convertion of GAO expressions into the set of WACFG productions is provided. The proposed algorithm turned out to be feasible in the context of parsing and translating Ukrainian Spoken and Ukrainian Sign language. The developed approach for mixed semantical and syntactical sentence parsing was tested on the database of sentences from Ukrainian fairy tail by Ivan Franko “Fox Mykyta” where 92 % of sentences were correctly parsed.uk_UA
dc.language.isoenuk_UA
dc.publisherCommission of Motorization and Energetics in Agricultureuk_UA
dc.subjectGrammatically augmented ontologyuk_UA
dc.subjectweighted affix context free grammaruk_UA
dc.subjectsemantic parsinguk_UA
dc.subjectsyntactic parsinguk_UA
dc.subjecttemplate productionsuk_UA
dc.titlePartial semantic parsing of sentences by means of grammatically augmented ontology and weighted affix context-free grammaruk_UA
dc.typeArticleuk_UA
dc.contributor.affiliationLviv Polytechnic National Universityuk_UA
dc.coverage.countryPLuk_UA
dc.format.pages27-32-
dc.relation.referencesen1. Dallin D. Oaks. 2010. Structural Ambiguity in English: An Applied Grammatical Inventory, 2 vols., Bloomsbury Academic. Reprint edition (February 23, 2012) London, p. 576. 2. Anisimov A., Marchenko O., Taranukha V., Vozniuk T. 2014. Development of a semantic and syntactic model of natural language by means of nonnegative matrix and tensor factorization. – in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), рр. 324–335. 3. Eddy S .R. 1994. RNA sequence analysis using covariance models / S. R. Eddy, R. Durbin // Nucleic Acids Research, Vol. 22, No. 11, рр. 2079–2088. 4. Koster C. H. A. 1991. Affix Grammars for natural languages / C. H. A. Koster // In: Attribute Grammars, Applications and Systems, International Summer School SAGA, Lecture Notes in Computer Science, Prague, Czechoslovakia. Vol. 545, рр. 469–484. 5. Oostdijk N. 1984. An Extended Affix Grammar for the English Noun Phrase / N. Oostdijk // In: Jan Aarts and Wim Meijs (eds), Corpus Linguistics. Recent Developments in the Use of Computer Corpora in English Language Research, Amsterdam: Rodopi. 6. Collins M. 2003. Head-driven statistical models for natural language parsing. In Computational Linguistics, рр. 589–638. 7. Blackburn P., Bos J. 2005. Representation and Inference for Natural Language: A First Course in Computational Semantics. CSLI Publications, Stanford. 8. Plank B. 2007. Sub-domain driven parsing. M. Sc. thesis. European Masters Program in Language and Communication Technologies (LCT). University of Amsterdam. 9. Wittgenstein L. 1958. Philosophical Investigations, trans. G.E.M. Anscombe, New York: Macmillan. 10. Landauer T. K. Foltz P. W., Laham D. 1998. An introduction to latent semantic analysis, Discourse processes, 25, рр. 259–284. 11. Jiang J. J., Conrath D. W. 1997. Semantic similarity based on corpus statistics and lexical taxonomy. Proc. of the Int'l. Conf. on Research in Computational Linguistics, рр. 19–33. 12. Rhee S. K., Lee J., Park M.-W. 2007. Ontologybased Semantic Relevance Measure. – Proceedings of the The First International Workshop on Semantic Web and Web 2.0 in Architectural, Product and Engineering Design, Busan, Korea. Access mode: http://ceur-ws.org/Vol-294/paper07.pdf. 13. Anisimov A., Marchenko O., Vozniuk T. 2014. Determining Semantic Valences of Ontology Concepts by Means of Nonnegative Factorization of Tensors of Large Text Corpora. Cybernetics and Systems Analysis, 50 (3), рр. 327–337. 14. Nagarajan M., Sheth A. P., Aguilera M., Keeton K., Merchant A., Uysal M. 2007. Altering Document Term Vectors for Classification – Ontologies as Expectations of Co-occurrence, рр. 1225–1226. 15. McDonald R., Pereira F., Ribarov K., Hajič J. 2005. Non-projective dependency parsing using spanning tree algorithms, Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, рр. 523–530. 16. Davydov M. V. 2014. A probabilistic search algorithm for finding suboptimal branchings in mutually exclusive hypothesis graph. Int. J. Know.- Based Intell. Eng. Syst. 18, 4, рр. 247–253. 17. Manning C. D., Schütze H. 1999. Foundations of Statistical Natural Language Processing, MIT Press, p. 680. 18. Unger C., Hieber F., Cimiano P. 2010. Generating LTAG grammars from a lexicon-ontology interface. In: Proceedings of the 10th International Workshop on Tree Adjoining Grammars and Related Formalisms (TAG+10), Yale University, рр. 61–68. 19. Kharbat F. 2011. A new architecure for translation engine using ontology:one step ahead. In Proc. of The International Arab Conference on Information Technology (ACIT'2011), рр. 169–173. 20. Miller G. A.1995. WordNet: A Lexical Database for English. Communications of the ACM, Vol. 38, No. 11, рр. 39–41. 21. Temizsoy M., Cicekli I. 1998. An Ontology Based Approach to Parsing Turkish Sentences. In: Proceedings of AMTA'98-Conference, Springer, October, Langhorne, PA, USA, рр. 124–135. 22. Davydov M., Lozynska O. 2015. Spoken and sign language processing using grammatically augmented ontology. Applied Computer Science. ACS journal, Poland, Vol. 11, Nо. 2, рр. 29–42. 23. Project UkrParser. 2015. Access mode: https://github.com/mdavydov/UkrParser. 24. Princeton University. 2010. About WordNet. Access mode: http://wordnet.princeton.edu.uk_UA
dc.citation.journalTitleEcontechmod-
dc.citation.volumeVolum 6, number 2-
dc.coverage.placenameLublin ; Rzeszowuk_UA
Розташовується у зібраннях:Econtechmod. – 2017. – Vol. 6, No. 2

Файли цього матеріалу:
Файл Опис РозмірФормат 
06-27-32.pdf161,01 kBAdobe PDFПереглянути/відкрити


Усі матеріали в архіві електронних ресурсів захищені авторським правом, всі права збережені.