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

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.