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Semi-automatic Emotion Recognition from Textual Input Based on the Constructed Emotion Thesaurus

Semi-automatic Emotion Recognition from Textual Input Based ...

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IEEE自然语言处理与知识工程国际学术会议

作者： shingo kuroiwa Department of Information Science and Intelligent System Faculty of Engineering The University of Tokushima 2-1Minami-josanjimaTokushima770-8506

＜正＞Natural language has attracted importance to some researchers working on Emotion recognizing. Our study focuses on the semi-automatic acquisition technique to obtain the emotion information with the constructed emotion thesaurus we have made. In this paper, we set up a system to recognize the affect sensing from the sentence or the text. During the building processing of our system, we firstly construct a Chinese language emotion thesaurus, the word in which thesaurus has its own classifying (such as commendatory, derogatory sense and neuter and the image value of the corresponding emotion information. Secondly, we set up the system to recognize the object sentence including functions of the syntax analysis, accidence analysis, emotion sensing and emotion computing. The following investigations are presented to show that the concept of "image value" can be used by the constructed system to obtain the emotion information from the sentence based on the constructed emotion thesaurus.

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Machine Transliteration

Machine Transliteration

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第20届亚太地区语言、信息和计算国际会议

作者： Mohamed Abdel Fattah shingo kuroiwa Faculty of Engineering University of Tokushima 2-1 Minantijosanjima Tokushima Japan 770-8506 Faculty of Engineering University of Tokushima 2-1 Minantijosanjima Tokushima Japan 770-8506

In the present study, we present different approaches for transliteration proper noun pair's extraction from parallel corpora based on different similarity measures between the English and Romanized Arabic proper nouns under consideration. The strength of our new system is that it works well for low-frequency words. We evaluate the presented new approaches using an English-Arabic parallel corpus. Most of our results outperform previously published results in terms of precision, recall and F- Measure.

关键词： Machine Transliteration

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