Chinese Grammatical Error Correction

A Methodological Guide to Datasets, Taxonomy, Evaluation, and Systems
Erscheinungsjahr 2027
ISBN: 978-3-032-45987-9
Verlag: Springer

Buch, Englisch, 172 Seiten, Format (B × H): 168 mm x 240 mm

Reihe: Synthesis Lectures on Computer Science

This book presents a research grounded and practice-oriented guide to Chinese grammatical error correction, with the goal of making the full pipeline understandable, reproducible, and extensible. Chinese grammatical error correction is now mature enough to have widely used datasets, established evaluation metrics, and a fast-moving system landscape, yet the field still lacks a single, coherent reference that connects these components. The need is particularly acute for Chinese, where segmentation, span definition, and reordering phenomena make grammatical error annotation decisions inseparable from evaluation outcomes and system design choices. By presenting the full workflow from data and annotation to metrics and modeling, the book consolidates a synthesis that is currently distributed across shared-task reports, general evaluation-metric documentation, and system papers. The authors do not treat Chinese grammatical error correction as an isolated task, but rather as a meeting point of linguistic analysis, annotation design, evaluation methodology, and system building. The book also offers practical grammatical error annotation guidance, including a clear contrast between manual schemes and automated pipelines, and an explicit treatment of how tokenization and segmentation reshape the notion of an edit. The book is ideal for readers who need both conceptual clarity and concrete procedures: graduate students entering the field, researchers who wish to compare approaches fairly, and engineers who require reliable computational pipelines.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Park, Jungyeul

Qiu, Mengyang

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
• Geisteswissenschaften Sprachwissenschaft Computerlinguistik, Korpuslinguistik

Inhaltsverzeichnis

Introduction.- CGEC Datasets.- CGEC Annotation.- CGEC Evaluation.- CGEC Systems.- Conclusion.

Über Autor(innen)

Jungyeul Park, Ph.D., a Research Professor at the Culture Technology Research Institute, Korea Advanced Institute of Science and Technology (KAIST), is a computational linguist and computer scientist specializing in syntactic parsing, formal grammar, and evaluation methodologies in natural language processing (NLP). His work includes the development of algorithms for dependency and constituency parsing, as well as the creation of multilingual treebanks and linguistically informed evaluation frameworks. He has published extensively on parsing, corpus development, and grammatical error correction, and recently introduced an alignment-based evaluation algorithm that addresses mismatches in preprocessing—critical for evaluating end-to-end NLP systems. His research blends theoretical precision with practical applications, particularly in multilingual and learner language contexts. Dr. Park collaborates widely on international projects and interdisciplinary initiatives that promote robust NLP tools and linguistic diversity. His work consistently aims to make language technologies more reliable and accessible across real-world settings.

Mengyang Qiu, Ph.D., an Assistant Professor in the Department of Speech-Language Pathology at Saint Elizabeth University, is a cognitive scientist and computational psycholinguist. His research includes two main lines of work: the first examines language and cognition, with an emphasis on cognitive aging, and the second focuses on natural language processing in educational settings, specifically grammatical error correction (GEC). This work includes developing linguistically informed frameworks for error annotation and evaluation in English, Chinese, and other typologically diverse languages, as well as exploring how large language models can be integrated into different stages of the GEC pipeline. Across this line of research, his goal is to develop scalable and interpretable methods that better support language learning and instruction.