Hyland & Tse (2007)指出，傳統的學術英語字彙表(例如：Coxhead，2000)忽略了一些重要的事實，即在不同的專業領域裡，學術字彙出現的比例不一，且常有不同的用法。舉例來說，在不同的學術領域中，學術字彙常與不同的字搭配出現，有時甚至呈現出不同的意義。因此，學術英語學習者需要的不是一套共用的學術英語字彙表，而是針對其學術領域特別整理的字彙知識。受到Hyland & Tse的啟發，我們發展了一項線上專業英語搭配詞搜尋工具，稱為TechCollo。運用TechCollo，學習者能夠比較在不同的專業英語語料庫裡，搭配詞出現的次數及使用的方式。此外，我們在此論文裡也使用TechCollo分析Coxhead的學術英語字彙在不同專業領域分佈的情形。我們的研究結果大致符合Hyland & Tse的論述，顯示學術英語字彙確實在不同專業領域出現的機率不一，且呈現明顯的搭配詞差異。

THE DEVELOPMENT OF A CORPUS-BASED TOOL FOR EXPLORING DOMAIN-SPECIFIC COLLOCATIONAL KNOWLEDGE IN ENGLISHSince it was published, Coxhead’s (2000) Academic Word List (AWL) has been frequently used in English for academic purposes (EAP) classrooms, included in numerous teaching materials, and re-examined in light of various domain-specific corpora. Although well-received, the AWL has been criticized for ignoring some important facts that words still tend to show irregular distributions and are used in different ways across disciplines (Hyland & Tse, 2007). One such difference concerns collocations. Academic words (e.g. analyze and concept) often co-occur with different words across domains and sometimes even refer to different meanings. What EAP students need, accordingly, is a “discipline-based lexical repertoire” (Hyland & Tse, p.235). Inspired by Hyland & Tse’s insightful remarks, we developed an online corpus-based tool, TechCollo, which is meant for EAP students to explore collocational knowledge in a domain or compare collocations across disciplines. TechCollo runs on textual data stored in three specialized corpora and utilizes frequency and some information-theoretical measures (e.g. mutual information) to decide whether co-occurring word pairs constitute collocations. In this article we describe the current version of TechCollo and how to use it in EAP studies. Particularly, we report a pilot study in which we employed TechCollo to investigate whether the AWL words take different collocates in different domain-specific corpora. This pilot basically confirmed Hyland & Tse’s indications and demonstrated that many AWL words show uneven distributions and collocational differences across disciplines.