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Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
Semantic Analysis: theory, applications and use cases
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Semantic Analysis: theory, applications and use cases

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Presentation we gave at 6th Seminar of Finnish-Russian University Cooperation in Telecommunications (FRUCT) Program organized by Nokia Research Center, Helsinki University of Technology, …

Presentation we gave at 6th Seminar of Finnish-Russian University Cooperation in Telecommunications (FRUCT) Program organized by Nokia Research Center, Helsinki University of Technology, Saint-Petersburg State University of Aerospace Instrumentation and sponsored by Nokia Siemens Networks, IEEE Russia (North West) Section, Nokia University Cooperation Program in Russia

www.fruct.org

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  • 1. Semantic analysis: theory,  applications and use cases 6th Seminar of FRUCT Dmitry Kan, Vladimir Poroshin Helsinki, 2009
  • 2. Road map • Math model of a Natural Language (NL) • Three levels of text analysis • Semantics vs syntax • Applications • Use cases
  • 3. Math model of a Natural Language • Backbone of NL: verbs + prepositions • Verb = F(arg1, …, argn) • Prepositions: 3D space (behind), time space (during), cause‐and‐effect relation (due to) • Class hierarchy (=world picture) • Basis functions • Words adjunction
  • 4. Math model: basis functions • Caus(x,y) = x causes y • Cont(x) = x continues • Hab(x,y) = x has y • Incep(x) = x begins • Oper(x,y) = x performs y • Lab(x,y) = x under action of y • Prepar(x) = prepare x, x is prepared • Fin(x) = x finishes, stops
  • 5. Math model: examples • Caus(Subj, Fin Lab(Accus, FIRE)) = to  extinguish a fire (=cause to stop having Subj  under action of fire) • Caus(Subj, Prepar(FOOD Accus)) = to stew the  vegetables (cause the Subj to get cooked) • Both examples map to the same Russian verb  ”тушить” => semantic disambiguation
  • 6. Road map • Math model of a NL • Three levels of text analysis • Semantics vs syntax • Applications • Use cases
  • 7. Three levels of analysing text • Morphological analysis: word level • Syntactic and semantic analysis: sentence  level • Object properties and relationships, anaphora  resolution: text level • Sentence = P(f1(x1,…,xn),…,fn(x1,…,xn)) – superposition of functions
  • 8. Three levels of analysing text Machinese Syntax is a syntactic parser that returns base forms and  compound structure, produces part‐of‐speech classes, inflectional  tags, noun phrase markers and syntactic dependencies.
  • 9. Road map • Math model of a NL • Three levels of text analysis • Semantics vs syntax • Applications • Use cases
  • 10. Semantics vs syntax • Он пришёл из вежливости (He came out of  courtesy) WHY? • Из (вежливость) generates ”WHY?” • Он пришёл из деревни (He came from a  village) WHERE FROM? • Из(деревня) generates ”WHERE FROM?”
  • 11. Road map • Math model of a NL • Three levels of text analysis • Semantics vs syntax • Applications • Use cases
  • 12. Applications • Intellectual search systems • Question‐answering systems • Spell checker • Summarization • Sentiment analysis • Machine Translation • Knowledge base • Facts extraction
  • 13. Road map • Math model of a NL • Three levels of text analysis • Semantics vs syntax • Applications • Use cases
  • 14. Use cases • Smart street dating: semantic search of the  best matching candidates around you • FAQ mobile agent: automatically suggest  solutions to the support requests • Sentiment recognition: goods evaluation • Automatic summarization: limit  information load on mobile devices
  • 15. References [1]Tuzov V. A.: Computer semantics of Russian language. Saint‐ Petersburg University Press, Saint‐Petersburg, 2004 (in  Russian). [2]Kan D. A., Lebedev I.S.: Method of formalizing semantical links  between objects in a natural language text. Bulletin of Saint‐ Petersburg University. Series 10. 2008. Issue 2. pp 56‐61 (in  Russian). [3]Kan D. A.: Method for automatic creation of translational  semantic dictionary for Machine Translation // XL Conference  Control Processes and Stability'09, pp. 429‐435 (in Russian). [4] www.semanticanalyzer.info
  • 16. Questions
  • 17. Thank you!

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