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  • 标题:The Application of Natural Language Processing and Automated Scoring in Second Language Assessment
  • 本地全文:下载
  • 作者:Heidi Han-Ting Liu
  • 期刊名称:Studies in Applied Linguistics & TESOL
  • 电子版ISSN:2689-193X
  • 出版年度:2012
  • 卷号:12
  • 期号:2
  • 页码:38-40
  • DOI:10.7916/salt.v12i2.1360
  • 出版社:Columbia University Libraries
  • 摘要:Natural language processing (NLP) is an area of research that is used to investigate the application of natural language and is the foundation of machine translation, natural language text processing, natural language generation, multilingual and cross language information retrieval, speech recognition, parsing, and expert systems. To understand natural language in order to build or select appropriate algorithms for processing, three major issues are called into attention: humans' thought processes, the meaning of linguistic input in context, and world knowledge. These considerations have led to the development of various types of NLP tools for lexical and morphological analysis, semantic and discourse analysis, as well as knowledge-based approaches (c.f., Chowdhury, 2003). After decades of evolution and advancement, the current stage of NLP, as Xi (2010) pointed out, has allowed language testing researchers to apply its techniques in developing automated scoring systems for the purpose of language learning and assessment.
  • 其他摘要:Natural language processing (NLP) is an area of research that is used to investigate the application of natural language and is the foundation of machine translation, natural language text processing, natural language generation, multilingual and cross language information retrieval, speech recognition, parsing, and expert systems. To understand natural language in order to build or select appropriate algorithms for processing, three major issues are called into attention: humans' thought processes, the meaning of linguistic input in context, and world knowledge. These considerations have led to the development of various types of NLP tools for lexical and morphological analysis, semantic and discourse analysis, as well as knowledge-based approaches (c.f., Chowdhury, 2003). After decades of evolution and advancement, the current stage of NLP, as Xi (2010) pointed out, has allowed language testing researchers to apply its techniques in developing automated scoring systems for the purpose of language learning and assessment.
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