It is generally agreed that collocational knowledge is an important language form for language learners in order for them to be proficient and fluent in the target language. However, previous studies have reported that second language (L2) learners lack collocational competence and they encounter difficulties in learning and using collocations. The present study not only investigates the overall collocational knowledge of Malaysian ESL learners, but more specifically, their productive and receptive knowledge of lexical and grammatical, which so far have not been further explored. Additionally, the ESL learners’ performance on three different types of collocations: verb-noun, adjective-noun, and verb-preposition is also investigated. Results of the study reveals a few interesting findings with respect to the Malaysian ESL learners’ overall knowledge of collocations, in particular their productive and receptive knowledge of collocations in relation to the three different types of collocations (verb-noun, adjective-noun, and verb-preposition). Pedagogical implications with regard to collocations and recommendations for future research are also put forward. investigated both the receptive and productive aspects of collocational.

This paper is concerned with the application of technologies developed in other disciplines, in particular with the use of text processing techniques to investigate the problems of second language learner writing in English. The question addressed is whether learner texts produced by L1-Malay learners at the University of Malaya can usefully be processed using the Constituent Likelihood Automatic Word-tagging System (CLAWS); a part-of-speech (POS) tagger developed for and trained on texts written by native speakers of the language. The study adopts the procedure employed by van Rooy and Schäfer (2002).CLAWS was used to automatically POS tag a subset of the Malaysian Corpus of Learner English (MACLE), and the texts were then analyzed for tagging accuracy.CLAWS was found to perform less well on learner text than on native speaker texts, but still with an accuracy rate of over 90%. The sources of error are traced, and spelling errors are found to be the most common source. Closer inspection indicates that successful tagging is likely to lead to problems downstream in later processing, which suggests that to optimize performance, some modifications will be required in tagger design.

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This paper presents the processes involved in the design and development of the Malaysian Corpus of Financial English (MaCFE); a specialized corpus containing a wide range of online/internet documents (i.e. communiqué) from various financial institutions in Malaysia. It describes in detail the processes involved in the collection and selection of data and preprocessing of raw data, which includes data digitizing, cleansing and tagging. This paper also introduces the user interface for MaCFE with its built-in linguistic analysis features. MaCFE was designed and developed with the intention of providing corpus linguistic researchers with the avenue to explore the field and for ESP/EAP practitioners in Malaysia, as the resources for the development of local-based ESP/EAP curriculum and teaching and learning materials. It would also serve as a learning avenue for future financial professionals in their training. MaCFE corpus has approximately 4.3 million words from 1472 electronic documents retrieved from banks and financial institutions' official websites. At present, users can make queries to the MaCFE database using its built-in concordancer. In the future, its language-data-processing facilities will be expanded to include tools for keyword, wordlist and word collocations queries.

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