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A Semantic Analyzer for Aiding Emotion Recognition in Chinese

A Semantic Analyzer for Aiding Emotion Recognition in Chinese. ICIC 2006. Jiajun Yan, David B. Bracewell, Fuji Ren, and Shingo Kuroiwa. Department of Information Science and Intelligent Systems Faculty of Engineering, The University of Tokushima. Introduction.

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A Semantic Analyzer for Aiding Emotion Recognition in Chinese

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  1. A Semantic Analyzer for Aiding Emotion Recognition in Chinese ICIC 2006 Jiajun Yan, David B. Bracewell, Fuji Ren, and Shingo Kuroiwa. Department of Information Science and Intelligent Systems Faculty of Engineering, The University of Tokushima.

  2. Introduction • Semantic analysis helps to understand the roles and relations between objects, humans, etc. in the sentence. • In this paper, we propose a system for understanding emotion in Chinese verbs. • The emotion “felt toward” and “felt by” can be known.

  3. The SEEN System

  4. Syntactic Analysis • Morphological Analysis • Zhang, H., Yu, H., Xiong, D., Liu, Q.: Hhmm-based Chinese lexical analyzer ictclas.In: the Second SIGHAN workshop affiliated with 41st ACL. (2003) • Based on Hidden Markov Model • Chinese Parsing • Zhou, Q.: A statistics-based Chinese parser. In: Proceedings of the Fifth Workshop on Very Large Corpora. (1997) • Because the parser was used on the Penn Chinese Treebank, and it is freely available

  5. Headword Assignment

  6. Structures

  7. Structures • Fig. 3. Another representation of a semantic dependency tree

  8. Functional Tags

  9. Semantic Dependency Assignment • Decision Tree Classifier • 4 Features • Phrase Type • Headword & Dependent • Headword & DependentPart-of-Speech • Context • Accuracy 84%

  10. Chinese Emotion Predicates

  11. Chinese Emotion Predicates

  12. Experimentation • 80 sentences (10 sentences per predicate) were collected and examined. • Negated emotions in English are not looked at. The semantic dependency was manually given. • The accuracy was 100%.

  13. Experimentation • Example of a Currently Unclassifiable Sentence

  14. SEEN System (After) • DT Classifier -> Probabilistic Classification • Add Rule-Based Correction • Accuracy increase to 85.1%

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