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- W2130607646 abstract "Many features have been proposed to evaluate examineespsila language proficiency. However, few of them are semantic based. In this paper, a novel feature for semantic scoring is presented. It is designed for a typical question type in language tests, namely reading-answering-problem. The proposed feature extraction process involves several operations: transcribing the speech data, automatically tagging the transcribed text and scoring the tagged text. The pattern based tagging is performed on the pre-designed Finite State Machines (FSMs) and the scoring fusion is based on the semantic calculations in a knowledge database. Experiment on Mandarin data validates the effectiveness of the semantic feature in the language proficiency evaluation." @default.
- W2130607646 created "2016-06-24" @default.
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- W2130607646 date "2008-01-01" @default.
- W2130607646 modified "2023-10-18" @default.
- W2130607646 title "Towards the Automatically Semantic Scoring in Language Proficiency Evaluation" @default.
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- W2130607646 doi "https://doi.org/10.1109/icalt.2008.58" @default.
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