EAGLE: an Error tAGger for Learners of English

Abstract

This paper describes the design and development of EAGLE, an Error tAGger for Learners of English. EAGLE combines all the processes necessary for the analysis of learners' language, such as creating an error tagset, tagging learners' writing, and reporting error tagging results, within the same tool. EAGLE has been developed to allow more flexibility in the creation of tagsets as well as to support many tagsets. These functionalities are achieved with the use of a hierarchical tree tagset and offset annotation. Researchers can develop their own tagsets from different theoretical frameworks and even apply them to the same document. EAGLE also provides multiple ways of viewing and comparing the statistics of tagged errors. All these features allow taggers to compare their ideas and work together to create an error tagset to render the analysis more reliable and accurate. Since EAGLE was not designed specifically for any learner language, it could be applied to tag errors produced by learners of other second languages as well.