Toward one-stop Information Mining: Tailoring Web Texts to Effective Machine Translation

[[abstract]]This paper proposes the tailoring of web texts in controlled language (CL) to meet the audience’s double expectations of “a-click-for-adequate-information” and “a-click-for-easy-understanding” through machine translation (MT) application, employing web texts on Taiwanese cuisine as a case study. Drawing on Grice’s cooperative principles as the theoretical framework, the merits of tailored web texts are justified by conducting a study of contrasting two sets of ten MTs of web texts with and without meeting Grice’s cooperative principles. The tailored web texts distinguish themselves from untailored web texts by presenting adequate information with thematic diversity, using headings as indexical references and clear, concise verbal presentations. Furthermore, a questionnaire-based survey shows that 85% of respondents favor MTs of tailored web texts and only 15% of them, MTs of untailored ones. The respondents dislike untailored texts for no clear, logical verbal presentation, lack of adequate headings as indexical references, many grammatical errors and ambiguous, incorrect words. In conclusion, to move toward “one-stop information mining” through MT application on the web, a suggestion is made that some existing web texts can be tailored in CL and be presented with special linguistic and pragmatic features as an effective and alternative way out