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Cognitive Approach to Japanese Constructional Phenomena: Evidence from Motion Construction and Resultative Construction

This paper explores the relationship between noun semantic class and sentence meaning in Japanese constructions, specifically focusing on motion and resultative constructions. Through a statistical analysis of corpus data and participant evaluations, the study aims to determine the qualitative character of Japanese language and the role of noun meaning in sentence construction.

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Cognitive Approach to Japanese Constructional Phenomena: Evidence from Motion Construction and Resultative Construction

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  1. International Conference on Japanese Language Education 2006 Columbia University, New York, USA Cognitive Approach to Japanese Constructional Phenomena: Evidence from Motion Construction and Resultative Construction Jae-Ho Lee and Hitoshi Isahara National Institute of Information and Communications Technology

  2. Background • Traditional approach to describing “sentence meaning” and controversy • Lexicalist Approach (Verb centrism): verb automatically dictates accompanying information ⇒Lexical Conceptual Semantics, Verb semantics: Levin 1993, Kageyama 1995 • Heuristic Approach: worldwide anti-lexicalist movement ⇒Construction Grammar: Fillmore et al. 1989, Goldberg 1995, 2006 • Abstract form of sentence encodes meaning e.g. “S-V-O-O” pattern means transportation of the thing • Current main trend is to study European and American languages; little research on Japanese language • Japanese requires specifying semantic class of noun • 太郎が神棚にお金をあげる・・・caused motion (使役移動) • 太郎が花子にお金をあげる・・・caused possession(使役所有)

  3. Introduction • Purpose • To review relationship between noun semantic class as natural classification (e.g., location, animate) and the meaning of entire sentence (e.g., motion, change) • To determine if construction research using a statistical method to understand deeper level of Japanese is possible • Method • Collecting KWIC data on “XがYにVする” from corpus • Asking participants to evaluate sentence meaning • Encoding noun semantic • Analyzing using decision trees.

  4. Introduction • Linguistic phenomenon • Meaning of noun is systematically related to sentence meaning • It is necessary to include noun semantic class in definitions of Japanese constructions or constructional sentence patterns • Methodology • Importance of multi-dimensional grasp of meaning • Our statistical method determines qualitative character of language.

  5. Data Collocation test • XがYに消える • 患者が診察室に消えた。 • エルフの船が光の中に消え、 • ゆうは闇に消えた。 • テールランプが闇に消えた。 • ヒュウガが地割れに消えた。 • ヒュウガがビルの中に消えた。 • この火が雨脚に消えた。 • 生活費が飲み代に消えた。 • 決勝点が幻に消えた • 思いが宙に消えた

  6. Data • XがYに消える • 患者が診察室に消えた。 • エルフの船が光の中に消え、 • ゆうは闇に消えた。 • テールランプが闇に消えた。 • ヒュウガが地割れに消えた。 • ヒュウガがビルの中に消えた。 • この火が雨脚に消えた。 • 生活費が飲み代に消えた。 • 決勝点が幻に消えた • 思いが宙に消えた

  7. Cluster Analysis(simple classification) Intransitive motion 移動 Autonomous disappearance 自律的自然消滅 Disappearance 消滅 Changing disappearance 変動的消滅

  8. Literature Review • Restriction 1 • What restriction? Verb or construction? • Construction model: constructional polysemy • Verb class model: lexical subordination • A) Analysis taken in advance of point under discussion B)Second-guesstheory ⇒Neither is sufficient ⇒New description theory necessary • Re-modeling by considering meaning of noun（cf. Generative lexicon）

  9. Literature Review • Restriction 2 • Nakamoto, Lee, Kuroda（2006）claim that noun semantic class is deeply related to sentence meaning

  10. Research Question • Is there any correlation between noun and entire sentence meaning? • How to confirm?

  11. Methods • Target of experiment • To investigate contribution of noun meaning to entire sentence using actual language data • Problems • Deciding sentence meaning • Describing noun in detail. • Determining contribution • Alternative plan • Adopt dominant descriptions as determined by six native speakers. • Describe by thesaurus-based semantic class (using 「日本語語彙大系(NTT)」) • Determine contribution of each element (decision tree) using statistical method

  12. Modeling • Cycle of analysis Sampling Coding Analysis Raw data Dependent variable Decision on sentence meaning Data analyzed by CHASEN Independent variable Decision tree analysis Data search by KH coder Identification of noun semantic

  13. Experiment data • Corpus：新潮文庫(Shincho Bunko)100冊 text edition • Size of corpus: • Total no. of words: 4,621,329 • Type frequency: 61,459 • Number of average appearances: 75.19 • Standard deviation: 2223.38 • Sampling • KWIC (Keyword in context) search of「XがYにVする」pattern: 2,745 token frequency examples were collected

  14. Frequency distribution Example

  15. Variable list • Category in natural classification:1. Agent 主体,2. Concrete object 具体物, 3. Abstract relation 抽象関係, 4. Event 事, 5. Location 場所, 6. Abstract object 抽象 • Origin and generation of object: 7. Natural object 自然物, 8. Artificial object 人工物, 9. Animate object 生物, 10. Inanimate object 無生物 • Variable to express X (が格): 6, 7, 8, 9, 10 • Variable to express Y (に格): 1, 2, 3, 4, 5, 6

  16. Statistical analysis • Decision Tree • A predictive model. It connects observations about an item to the item's target value. • Each interior node corresponds to a variable; an arc to a child represents a possible value of that variable. • A leaf represents the predicted value of target variable given values of variables represented by the path from the root. http://en.wikipedia.org/wiki/Decision_Tree

  17. Evaluationby six participants • Six possible meanings • 様態(depictive), 存在(existence), 移動(motion), 知覚(perception), 変化(change), 働きかけ(causative) • Participants decisions varied • ⇒ Necessary to differentiate. ⇒ Agreement rate used to “weight”

  18. Parameters • Dependent variables • Nominal value {depictive, existence, motion, perception, change, causal} • Independent variables • Nominal value of noun semantic class • Weighting:evaluationagreement rate • Algorithm for decision tree: CHAID（chi-squared automatic interaction detection)

  19. Frequency distribution of evaluations 一都市の陥落が一国家の滅亡につながる 女が砂に埋まって・・ 彼女は玄関に駆け込んだ。 父の目が怒りに燃えている エーゲ海が南にある。 過去にさかのぼったような感覚が私の中にあった。 Motion Existence Perception Causal Change Depictive

  20. Agreement rate of evaluation Agreement rate is high and result of evaluation is steady Agreement rate is low and result of evaluation is unstable

  21. Decision tree Y:[±location] Motion construction Y:[±agent] Y: [±concrete object] Y[±abstract object] Perception construction X:[±animate] Causative construction Depictive construction Change construction

  22. Classification result Observation value

  23. Classification result Observation value

  24. Discussion • Results • Motion construction is very steady • Attribute of Y as location influences the divergence of entire data • Attribute of an abstract relation and event does not influence divergence of entire data • The motion construction and existence construction cannot be distinguished by noun semantic class alone • Agent and concrete objects of Y are important in perception construction • Change construction and depictive construction are separated by semantic class of subject ⇒ Noun meaning class of a noun is systematically related to sentence meaning

  25. Conclusion • Defining Japanese construction or constructional sentence patterns requires information related to the meaning class of the noun.

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