Computational and Corpus Approaches to Chinese Language Learning

著者名：Lu, Xiaofei (EDT)/Chen, Berlin (EDT)

Springer（2019/02/06発売）

• 言語：ENG
• ISBN：9789811335693
• eISBN：9789811335709

Description

This book presents a collection of original research articles that showcase the state of the art of research in corpus and computational linguistic approaches to Chinese language teaching, learning and assessment. It offers a comprehensive set of corpus resources and natural language processing tools that are useful for teaching, learning and assessing Chinese as a second or foreign language; methods for implementing such resources and techniques in Chinese pedagogy and assessment; as well as research findings on the effectiveness of using such resources and techniques in various aspects of Chinese pedagogy and assessment.

Table of Contents

Part I Introduction.- Computational and Corpus Approaches to Chinese Language Learning: An Introduction.- Usage-based Theory of Language Learning Necessitates Computational and Corpus Methods in Language Teaching.- The Corpus Approach to the Teaching and Learning of Chinese as an L1 and an L2 in Retrospect.- Part II Tools, Resources and General Applications.- Academic Chinese: From Corpora to Language Teaching.- Pedagogical Applications of Chinese Parallel Corpora.- Data-driven Adapting for Fine-tuning Chinese Teaching Materials: Using Corpora as Benchmarks.- Part III Specific Applications.- Context Analysis for Computer-Assisted Near-Synonym Learning.- Visualization of Stylistic Differences between Chinese Synonyms.- Using Corpus-based Analysis of Neologisms on China's New Media for Teaching.- Part IV Learner Language Analysis and Assessment.- Acquisition of the Chinese Particle le by L2 Learners: A Corpus-based Approach.- Mandarin Chinese Mispronunciation Detection and Diagnosis Leveraging Deep Neural Network based Acoustic Modeling and Training Techniques.- Automated Chinese Error Diagnosis for Language Learners: Resources and Evaluations.- Automated Chinese Essay Scoring based on Multi-level Linguistic Features.