Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICALT.2008.58 | ICALT |
Keywords | Field | DocType |
finite state machines,semantic feature,transcribed text tagging,knowledge database,reading-answering-problem,novel feature,semantic calculation,semantic scoring,feature extraction process,scoring fusion,call,speech data transcribing,pattern based tagging,feature extraction,proposed feature extraction process,automatically semantic scoring,language proficiency evaluation,language test,language proficiency,mandarin data,tagged text scoring,natural language processing,computer aided instruction,computer aided language learning,automation,speech processing,automata,natural languages,speech,correlation,testing,data validation,databases,finite state machine | Speech processing,Language proficiency,Computer science,Finite-state machine,Feature extraction,Natural language,Artificial intelligence,Natural language processing,Semantic feature,Semantic computing,Semantic compression | Conference |
ISBN | Citations | PageRank |
978-0-7695-3167-0 | 0 | 0.34 |
References | Authors | |
4 | 2 | |