We evaluate the effect of adding parse features to a leading model of preposition usage. Results show a significant improvement in the preposition selection task on native speaker text and a modest increment in precision and recall in an ESL error detection task. Analysis of the parser output indicates that it is robust enough in the face of noisy non-native writing to extract useful information.

collocations are one of the areas generally considered problematic for efl learners. iranian learners of english like other efl learners face various problems in producing oral collocations.  an analysis of learners' spoken interlanguage both indicates the scope of the problem and the necessity to spend more time and energy by learners on mastering collocations. the present study specifically f...

We present the first corpus annotated with preposition supersenses, unlexicalized categories for semantic functions that can be marked by English prepositions (Schneider et al., 2015). That scheme improves upon its predecessors to better facilitate comprehensive manual annotation. Moreover, unlike the previous schemes, the preposition supersenses are organized hierarchically. Our data will be p...

This paper describes a maxent-based preposition sense disambiguation system entry to the preposition sense disambiguation task of the SemEval 2007. This system uses a wide variety of semantic and syntactic features to perform the disambiguation task and achieves a precision of 69.3% over the test data.

This paper presents research on building a model of grammatical error correction, for preposition errors in particular, in English text produced by language learners. Unlike most previous work which trains a statistical classifier exclusively on well-formed text written by native speakers, we train a classifier on a large-scale, error-tagged corpus of English essays, relying on contextual and g...