Developing and evaluating a Chinese collocation retrieval tool for CFL students and teachers

Abstract

The development of collocational knowledge is important for foreign language learners; unfortunately, learners often have difficulties producing proper collocations in the target language. Among the various ways of collocation learning, the DDL (data-driven learning) approach encourages the independent learning of collocations and allows learners to directly use corpora and tools to search for proper collocations. There are several useful collocation tools (JustTheWord, COCA, Tango, Gutenberg Collocation Tool) available for English as a second language (ESL) learners. There are, however, very few Chinese collocation tools available for learners of Chinese as a foreign language (CFL), despite the increasing numbers of CFL learners within the past several years. To help CFL students and teachers efficiently search for proper collocates, this paper introduces a new web-based collocation retrieval tool, ICE (Intelligent Collocation Engine), which is based on a large part-of-speech-tagged Chinese news corpus. To determine if the new tool can facilitate the searching of collocations, this tool was tested by a group of CFL students to find proper collocates in a translation task. The results showed that the students who used the ICE tool could successfully found many proper Chinese collocates for a given noun. In addition, 12 in-service CFL teachers were also invited to evaluate the effectiveness of this collocation tool. These teachers indicated that they could find proper Chinese collocates easily with the help of ICE. The teachers also commented that they might use the collocation retrieval tool to prepare their teaching materials. Both findings of the experiment as well as the survey suggest that the new collocation retrieval tool can facilitate Chinese collocation teaching and learning, but the content and functions of this tool can be further enhanced. The findings of this study can be useful for language teachers, researchers, and developers of corpus-based learning tools.

Keywords:

• Chinese learning
• Chinese collocations
• collocation retrieval tool
• large tagged corpus

Additional information

Funding

This research is partially supported by the “Aim for the Top University Project” of National Taiwan Normal University (NTNU), sponsored by the Ministry of Education, Taiwan, Republic of China and the “International Research-Intensive Center of Excellence Program” of NTNU and National Science Council, Taiwan, Republic of China [grant number NSC 103-2911-I-003-301].

Notes on contributors

Howard Hao-Jan Chen

Howard Hao-Jan Chen is a professor of English Department at National Taiwan Normal University, Taipei, Taiwan, Republic of China. He also serves as director of Mandarin Training Center at National Taiwan Normal University. His research interests include computer-assisted language learning, corpus research, and second language acquisition.

Jian-Cheng Wu

Jian-Cheng Wu is a researcher at Natural Language Processing Lab at National Tsing-Hua University, Hsin-Chu, Taiwan, Republic of China. His research interests include corpus linguistics, natural language processing, and computer-assisted language learning.

Christine Ting-Yu Yang

Christine Ting-Yu Yang is a graduate student at National Taiwan Normal University, Taipei, Taiwan, Republic of China. Her research interests include computer-assisted language learning and corpus research.

Iting Pan

Iting Pan is a research assistant at National Taiwan Normal University, Taipei, Taiwan, Republic of China. Her research interests include teaching Chinese as a second language and learner corpus research.