Writing assistants and automatic lexical error correction: word combinatorics

Genuine lexical writing assistants that attempt to detect lexical errors such as miscollocations are traditionally less common in Computer Assisted Language Learning than spell and grammar checkers. However, there is empirical evidence of the importance of capturing and correcting miscollocations in the writings of language learners, and therefore an increasing number of proposals deals with the detection of errors in collocations and the delivery of lists of correction suggestions. However, very few of these proposals take into account the varying ease with which learners can master different collocation and miscollocation types, or the fact that some collocation type errors might be more common than others, given that a writing assistant should be capable of handling at least the most common types of miscollocation. Furthermore, existing proposals explore collocation error-specific strategies, implicitly assuming that with one universal strategy all types of miscollocations can be detected and corrected. Our preliminary study, conducted on Spanish and French material, highlights one type of collocation in which learners err the most: support verb constructions (SVCs). To account for this, we explore a SVC-specific collocation error detection and correction strategy.