A SHORT GUIDE TO THE MEANING-TEXT LINGUISTIC THEORY

1. A SHORT GUIDETO THE MEANING-TEXT LINGUISTIC THEORY JASMINA MILIĆEVIĆ DALHOUSIE UNIVERSITY - HALIFAX (CANADA) 2006, Journal of Koralex, vol. 8: 187-233

2. Contents 0. Introduction (1-2) • Postulates and methodologicalprinciple (2-4) • Meaning-Textmodels (4-6) • Illustration of the linguisticsynthesis in the Meaning-Textframework (6-27) • Summary of MTT’s main features (27-30) • Basic Meaning-Textbibliography(30-36)

3. 0. Introduction • MTT = theoreticalframework for the construction of models of languages • Launched in Moscow (Žolkovskij & Mel’čuk 1967) • Developed in Russia, Canada, Europe • Formalcharacter computer applications • Relatively marginal

4. 1. Postulate 1 • “Natural language is (considered as) a many-to-many correspondence between an infinite denumerable set of meanings and an infinite denumerable set of texts.” (2) {SemRi} <=language=> {PhonRj} │0 < i, j ∞

5. Postulate 2 • “The Meaning-Text correspondence is described by a formal device which simulates the linguistic activity of the native speaker—a Meaning-Text Model.”(3)

6. Postulate 3 • “Given the complexity of the Meaning-Text correspondence, intermediate levels of (utterance) representation have to be distinguished: more specifically, a Syntacticand a Morphologicallevel.”(3)

7. Methodologicalprinciple • “The Meaning-Text correspondence should be described in the direction of synthesis, i.e., from Meaning to Text (rather than in that of analysis, i.e., from Text to Meaning).” (3)

8. WHY? • Producing speech is an activity that is more linguistic than understanding speech; • Some linguistic phenomena can be discovered only from the viewpoint of synthesis (ex: lexical co-occurrence = collocations). • Corollary: • study of paraphrases (and lexicon) occupies a central place in the M-T framework.

9. Paraphrase • Synonymy = fundamental semantic relation in natural language  “to model a language means to describe its synonymic means and the ways it puts them in use”. • Meaning = invariant of paraphrases • Text = “virtual paraphrasing” • Lexical paraphrase  semantic decomposition of lexical meanings

10. Semantic decomposition of ‘criticize’ • (definiendum): ‘X criticizes Y for Z’ • ≈ (definiens): • ‘Y having done21Z which X considers2 bad2 for Y or other people1, • and X believing3 that X has good11 reasons12for considering2 Z bad2, || • X expresses31X’s negative11opinion1 of Y because of Z(Y), • specifying what X considers2 bad2 about Z, • with the intention2 to cause2that people1 (including Y) do not do21Z.’

11. 2. Meaning-TextModels: Characteristics • Equative = transductive generative (Postulate 1) • Completelyformalized (Postulate 2) • Stratificational model (Postulate 3)

12. MTM Architecture (Neuvel)

13. Representations Neuvel.net, (adapted from Mel'chuk 1988: 49)

14. 2. MTM: peripheral structures • Reflectdifferentcharacerizations of the central entity= provideadditional information relevant ateachlevel. • Peripheral: they do not existindependently of the central structure. • Purpose: to articulate the SemSinto a specific message, by specifyingthe wayitwillbe ‘packaged’ for communication.

15. Central and peripheral S / level of R • SemR = <SemS, Sem.CommS, RhetS, RefS> • DSyntR = < DSyntS, Dsynt-CommS, DSynt.-ProsS, Dsynt-AnaphS) • SSyntR = <SSyntS, SSynt-CommS, SSynt-ProsS, SSynt-AnaphS> • DMorphR = <DMorphS, Dmorph-ProsS>

16. 2. MTM: rules

17. 3. Illustration: LinguisticSynthesis • Synthesis: 1 SemR (X 2)  3 PhonR (X 2) • SemR [1]: Theme = mediaPhonR (1 a, b, c) • SemR [2]: Theme = decisionPhonR(2 a, b, c)

18. SemR’s central structure = SemS • A SemSrepresents the propositionalmeaning of a set of paraphrases. • SemS = network: nodes and arcs • Nodes: labeledwithsemantemes. • Arcs: labeledwithnumbers (predicate-argument relations).

19. SemS (example)

20. Peripheral structure Sem-CommS • Sem-CommSrepresents the communicative intent of the Speaker. • Formally, Sem-CommS = division of the SemSinto communicative areas, eachmarkedwith one of mutually exclusive values.

21. Eight communicative oppositions • Thematicity = {Theme, Rheme, Specifier} • Giveness = {Given, New} • Focalization = {Focalized, Non-Focalized} • Perspective = {Backgrounded, Foregrounded, Neutral} • Emphasis = {Emphasized, Neutral} • Assertiveness = {Asserted, Presupposed} • Unitariness = {Unitary, Articulated} • Locutionality = {Communicated, Signaled, Performed}

22. Otherperipheral Sem-structures • Sem-RhetSrepresents the Speaker’srhetoricalintent. • Sem-RefS = set of pointers fromsemantic configurations to the correspondingentities in the real world.

23. Theme: media • a. [The media]T[harshlycriticized the Government for itsdecision to increaseincome taxes]R b. [The media]T[seriouslycriticized the Government for itsdecision to raiseincome taxes]R c. [The media]T[leveledharshcriticismat the Government for itsdecision to increaseincome taxes]R

24. Theme = Media

25. Theme = government’sdecision • a. [The government’sdecision to increaseincome taxes]T[wasseverelycriticized by the media]R b. [The government’sdecision to raiseincome taxes]T[drewharshcriticismfrom the media]R c. [The government’sdecision to increaseincome taxes]T[came underharshcriticismfrom the media]R

26. Theme = government’sdecision

27. Syntacticdependency • Relation of strict hierarchy • Characteristics: • Antireflexive • Antisymmetric • Antitransitive

28. Syntactic structure • Tree • Nodes labeled with lexical units; not linearly ordered • Top node does not depend on any lexical unit in the structure, while all other units depend on it, directly or indirectly. • Arcs (= branches) labeled with dependency relations

29. DSyntS • Nodes: labeledwithdeep lexical units (≠ pronouns and ‘structural words’) subscripted for all meaning-bearinginflections. • Branches: labeledwithnames of deepsyntacticdependency relations. • Deep lexical unit = lexeme, (full) phraseme or name of a lexical function.

30. Lexical functions • LF = formaltoolsused to model lexical relations, i.e., restricted lexical co-occurrence (= collocations), and semanticderivation. Theyhave different lexical expressions contingent on the keyword. • LF corresponds to a meaningwhose expression isphraseologicallybound by a particularlexeme L (= argument of the LF).

31. Lexical functions: examples • Magn ‘intense/very’ • Magn(wind) = strong, powerful • Magn(rain(N)) = heavy, torrential // downpour • Magn(rain(V)) = heavily, cats and dogs • S1 ‘person/objectdoing L’ • S1(crime) = author, perpetrator [of ART ˷ ] // criminal • S1(kill) = killer

32. Lexical functions: classification • According to theircapacity to appear in the textalongside the keywords: syntagmatic (normally do) and paradigmatic (normally do not) • According to theirgenerality/universality: standard (general/universal) and non-standard (neithergeneralnoruniversal) • According to theirformal structure: simple and complex

33. Examples • Magn: syntagmatic, standard, simple LF • S1: paradigmatic, standard, simple LF • A YEAR that has 366 days= leap [˷] = non-standard LF: itonlyapplies to one keyword (year) and has just one value (leap); not universal (not valid cross-linguistically) • CausePredPlus: complex LF

34. LFsrealized in (1) and (2) • Magn(criticize) = bitterly, harshly, seriously, strongly // blast • Magn(criticism) = bitter, harsh, serious, severe, strong • CausePredPlus(taxes) = increase, raise • QSØ(criticize) = criticism • QSØ(decide) = decision • Oper1(criticism) = level[˷ at N|N denotes a person], raise[˷ against N], voice[˷] • Oper2(criticism) = come[under˷], draw[˷ from N], meet[with˷]

35. Deep lexical units • Do not correspond one-to-one to the surface lexemes: in the transition towards surface syntax, somedeep lexical unitsmaygetdeleted or pronominalized and some surface lexemesmaybeadded.

36. 12 Deep-Syntactic Relations • 6 actantialDSyntRels (I, II, III,…, VI) + 1 DSyntRel for representing direct speech (=variant of DSyntRel II) • 2 attributive DSyntRels: ATTRrestr(ictive) and ATTRqual(ificative) • 1 AppenditiveDSyntRel (APPEND): links the Main Verb to ‘extra-structural’ sentence elements (sentential adverbs, interjections,…) • 2 coordinative DSyntRels: COORD and QUASI-COORD

37. DSyntR – (1a)

38. DSyntR– (1b)

39. DSyntR– (1c)

40. Semantic module:correspondencerules • Lexicalizationrules • Morphologizationrules • Arborizationrules • Communicative rules • Prosodicrules

41. SemR[1] DSyntRs (1a) and (1b)

42. Semantic module: equivalencerules • = paraphrasingrules • Semanicequivalencerules equivalencebetween (fragments of) 2 SemRs • Lexico-syntacticrules: formulated in terms of lexical functions equivalencebetween (fragments of) 2 DSyntRs.

43. Ex.: lexical-syntacticequivalencerule

44. From D to SSyntR: the Deep-Syntactic module • SSyntS: dependencytree; nodeslabeledwithactuallexeme; branches labeledwithnames of languagespecific surface-syntacticdependency relations. • DSyntS≠ SSyntS: • Lexically: onlysemantically full lexemesvs all lexemes (including full and structural words + pronouns) • Syntactically : onlyuniversaldependency relations vs specificdependency relations

45. DSyntR / SSyntR (1a)

46. SSyntR (1b)

47. SSyntR (1c)

48. Deep-Syntactic module: major types of rules • Phrasemicrules • Deep-Syntacticrules • Pronominalizationrules • Ellipsisrules • Communicative rules • Prosodicrules

49. 6 phrasemicrules (1 a-c) • SSyntS (1a) • 1) Magn(CRITICIZE) <=> harshly; • 2) CausPredPlus(TAXES) <=> increase • SSyntS (1b) • 3) Magn(CRITICIZE) <=> seriously; • 4) CausPredPlus(TAXES) <=> raise • SSyntS (1c) • 5) Oper1(CRITICISM) <=> level; • 6) Magn(CRITICISM) <=> harsh

50. Constraints: examples • (3) a. The media raised harsh criticism against the Government for its decision to impose highertaxes. / The media leveled harsh criticism at the Government for its decision to impose higher taxes. • b. The media raised harsh criticism against the Government’s decision to impose higher taxes. vs. *The media leveled harsh criticism at the Government’s decision to impose higher taxes. • (4) ?The media raised harsh criticism against the Government for its decision to raise taxes.