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Fuzzy Semantic Grammar

Fuzzy Semantic Grammar. Motivations : Easy to encode domain knowledge Strong to parse NL sentence Useful for speech recognition. Jiping Sun October 2003. Semantic Grammar Formalism.

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Fuzzy Semantic Grammar

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  1. Fuzzy Semantic Grammar • Motivations : • Easy to encode domain knowledge • Strong to parse NL sentence • Useful for speech recognition Jiping Sun October 2003

  2. Semantic Grammar Formalism • FSG = { (A) Lexicon : +(w) C, • (B) Templates : T =C1…Cj … Ck • (C)I.M.1 Rules : C {wk|wi…wj} • (A), (B): expressing world knowledge in grammar. • (B), (C): enabling speech recognition W.L.2 search. • (C): generating natural language complexity. • I.M. = Information mass, • 2. W.L. = word lattice Jiping Sun October 2003

  3. Example-1 Natural language sentence : “A ship carrying more than seventy-five million liters of oil sank November nineteenth in waters near the coast of northwestern Spain.” (source:VOA ENVIRONMENT REPORT, Nov. 29, 2002) Jiping Sun October 2003

  4. Example-1 Template with lexeme : [A ship]1 carrying more than seventy-five million liters of oil [sank]2 November nineteenth in waters [near the coast of northwestern Spain]3. T = 1[Vehicle] + 2[Movement] + 3[Location] Jiping Sun October 2003

  5. Example-1 Information-mass rules : [vehicle]{ ship | carry[substance]} [substance]{ oil |[quantity]of } [quantity]{ liter |[numerical]} [A ship]carrying more than seventy-five million liters of oil [sank] November nineteenth in waters [near the coast of northwestern Spain]. Jiping Sun October 2003

  6. Example-1 Information-mass rules : [movement]{ sank |[time]} [time]{ [month] | [date] } [location]{ Spain | [direction] [geo_part]} [geo-part] { coast | in waters, near the } [A ship] carrying more than seventy-five million liters of oil [sank]November nineteenth in waters[nearthe coast of northwesternSpain]. Jiping Sun October 2003

  7. Example-2 Natural language sentence : “The Bush administration has announced new proposals designed to ease controls on industrial pollution.” (source:VOA ENVIRONMENT REPORT, Dec. 13, 2002) Jiping Sun October 2003

  8. Example-2 Template with lexeme : The Bush [administration]1 has [announced]2 new [proposals]3 designed to ease [controls]4 on industrial pollution. T = 1[Org] + 2[Issue] + 3[Doc] + 4[Purpose] Jiping Sun October 2003

  9. Example-2 Information-mass rules : [Org]{ administration | [leader]} [Issue]{ announce |[modal_time] } [Doc]{ proposal | new} The Bush[administration]has[announced]new[proposals] designed to ease [controls]on industrial pollution . Jiping Sun October 2003

  10. Example-2 Information-mass rules : [doc]{ proposal | gen::designed to} [purpose]{ control |on industrial pollution } [purpose]{ control | deg::ease} The Bush [administration] has [announced] new [proposals] designedto ease[controls]on industrial pollution . Jiping Sun October 2003

  11. World Knowledge & Language • Templates encode core knowledge • Categories are semantically defined E.g. Vehicle Move in-Timeat-Location Org Issue Docto-Purpose Pollution can-be controlled Jiping Sun October 2003

  12. World Knowledge & Language (2) • I.M. rules expand core knowledge • Constructs: specificity, auxiliary-ness … E.g. IM(Spain)>IM(coast)>IM(water) | Location (spec) IM(control) > IM(ease) > IM(try) | purpose (auxi) Jiping Sun October 2003

  13. FSG for Speech Recognition • Word lattice re-scoring is important • Templates search key word string • I.M. rules search for other words • This scheme can tolerate SR errors order of search : A shipcarryingmore than seventy-five millionliters of oilsank November nineteenthin watersnear the coast of northwestern Spain Jiping Sun October 2003

  14. Fuzzy Natural Language Grammar • NL is a very flexible system • Events can be rendered in arbitrary detail • To deal with it, use fuzzy rules • Fuzzy lexicons: • Fuzzy semantic templates: • Fuzzy information mass rules: w  C [0,1] Ci Tj[0,1] w | Ci(C )[0,1] Jiping Sun October 2003

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