Correcting Misuse of Verb Forms

Proceedings of ACL-08: HLT, pages 174–182, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics Correcting Misuse of Verb Forms John Lee and Stephanie Seneff Spoken Language Systems MIT Computer Science and Artificial Intelligence Laboratory Cambridge, MA 02139, USA {jsylee,seneff}@csail.mit.edu Abstract This paper proposes a method to correct En- glish verb form errors made by non-native speakers. A basic approach is template match- ing on parse trees. The proposed method im- proves on this approach in two ways. To improve recall, irregularities in parse trees caused by verb form errors are taken into ac- count; to improve precision, n-gram counts are utilized to filter proposed corrections. Evaluation on non-native corpora, represent- ing two genres and mother tongues, shows promising results. 1 Introduction In order to describe the nuances of an action, a verb may be associated with various concepts such as tense, aspect, voice, mood, person and number. In some languages, such as Chinese, the verb itself is not inflected, and these concepts are expressed via other words in the sentence. In highly inflected lan- guages, such as Turkish, many of these concepts are encoded in the inflection of the verb. In between these extremes, English uses a combination of in- flections (see Table 1) and “helping words”, or aux- iliaries, to form complex verb phrases. It should come as no surprise, then, that the mis- use of verb forms is a common error category for some non-native speakers of English. For example, in the Japanese Learners of English corpus (Izumi et al., 2003), errors related to verbs are among the most frequent categories. Table 2 shows some sentences with these errors. Form Example base (bare) speak base (infinitive) to speak third person singular speaks past spoke -ing participle speaking -ed participle spoken Table 1: Five forms of inflections of English verbs (Quirk et al., 1985), illustrated with the verb “speak”. The base form is also used to construct the infinitive with “to”. An exception is the verb “to be”, which has more forms. A system that automatically detects and corrects misused verb forms would be both an educational and practical tool for students of English. It may also potentially improve the performance of ma- chine translation and natural language generation systems, especially when the source and target lan- guages employ very different verb systems. Research on automatic grammar correction has been conducted on a number of different parts-of- speech, such as articles (Knight and Chander, 1994) and prepositions (Chodorow et al., 2007). Errors in verb forms have been covered as part of larger sys- tems such as (Heidorn, 2000), but we believe that their specific research challenges warrant more de- tailed examination. We build on the basic approach of template- matching on parse trees in two ways. To improve re- call, irregularities in parse trees caused by verb form errors are considered; to improve precision, n-gram counts are utilized to filter proposed corrections. We start with a discussion on the scope of our 174 task in the next …

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