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Mood Particles In The Chengdu Dialect-the Clustering And Association-Mining Based On Multi-Features Corpus

Posted on:2019-01-16Degree:DoctorType:Dissertation
Country:ChinaCandidate:Z N ChenFull Text:PDF
GTID:1365330572967995Subject:Linguistics and Applied Linguistics
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Mood involves a number of factors,including structure,meaning,and context.The function of multiple components is not exclusive but compatible/incompatible.With a high degree of fuzziness,The study of mood particles is very difficult.There’s a lot of controversy among different scholars and Introspection research is limited by the theoretical framework preinstalled.A precise definition is not enough to explain the complexity of a modal word.Therefor,A Corpus-based approach is proposed to study mood word systems.We use the multidimensional features of 11 dimensions and 42 features to label sentences.Determining Association by calculating weights based on Game Theory and clustering based on inclined similarity measures,The cluters and association rules of each particle are obtained.Then We will suggest the core function of each particles and study the system of them as a whole.Mood system is a Multidimensional hierarchical structure.We obtain the functional structure of Chengdu final particles:Form(adhesion)>Speech acts>Determine>Evaluation>Force.Mood system is a Multidimensional hierarchical structure.We obtain the functional structure of Chengdu final particles:Form(adhesion\distribution\sentence type)>Speech acts(specific speech-acts\degree)>Determine(expectation)>Evaluation(feeling\stance)>Force(emotion).The mood system of Chengdu dialect is driven by three "wheels":expectation,stance and emotion.SUO,strong mirativity and rhetorical question;WA,strong mirativity + rhetorical question and week mirativity + ask for proof;O and A,strong mirativity + enforcing information;NAN,enforcing question + strong mirativity;S(?),strongly indicating the hearer’s expection is wrong + alignment;HA,indicationg the hearer’s expection is strong + alignment;KA,alignment + strong emotion;NE,indicative + affirment +non-alignment;MA,strong emotion + alignment;BA,week emotion + alignment.The core functions of each modal word can be distinguished from each other,and each of them has its own functions,which have different functional identifications in three aspects:quantitative,contrastive and qualitative.The dimensions are still interrelated.The particles with independecy and thoese located at the beginning of a senteces;in a sentences or in more than one sentences intend to embody innterpersonal interaction and discourse function.Chengdu dialect has no special interrogative mood particles.The functions of Q-sentences is complicated.More than half of Q-sentences are rhetorical questions,and less than half of those are ’real’ ones.The emotion of the rhetorical question is the strongest and imply negative.Accidents against the expectations of the speaker are the most likely to trigger strong emotions,followed by confirmation of the hearer’s expectation error,followed by compliance with the listener’s expectation,and then confirmation/compliance with a third party’s expectations.Middle and weak emotions come from expectations of the speaker and the unexpectations.The allignment is unmarked in mood particles,and the distribution of emotion and emotion is complex.The anti-alignment is marked,emotional negative and emotional strong.The non-aligntment is also marked for mood particles.It appears in narrative discourse and self-talk without a true listener and tends to be neutral.Negative feelings are the most likely to trigger strong emotions,followed by positive ones,and neutral feelings tend to be non-strong emotions.Association makes the function of each modal particles "fuzzy".Undering the influence of diachronic evolution,language contact and personal idiom,mood particles are more or less overlapped in almost every function,and may have subtle differences hat depend on the quantitative differences of core functions.
Keywords/Search Tags:Chengdu dialect, mood particles, expectation, stance, multidimensional features, clustering, association, game, inclined similarity measures
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