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 SystemsFaculty of Engineering, The University of Tokushima.

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.

The SEEN System

Syntactic Analysis

• Morphological Analysis– Zhang, H., Yu, H., Xiong, D., Liu, Q.: Hhmm-based Chinese lexi

cal 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

Headword Assignment

Structures

Structures

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

Functional Tags

Semantic Dependency Assignment

• Decision Tree Classifier– 4 Features

• Phrase Type• Headword & Dependent• Headword & Dependent

Part-of-Speech• Context

– Accuracy 84%

Chinese Emotion Predicates

Chinese Emotion Predicates

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%.

Experimentation

• Example of a Currently Unclassifiable Sentence

SEEN System (After)

• DT Classifier -> Probabilistic Classification

• Add Rule-Based Correction

• Accuracy increase to 85.1%