Detecting errors in part-of-speech annotation
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Web-based models for natural language processing
ACM Transactions on Speech and Language Processing (TSLP)
Detecting errors in discontinuous structural annotation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
The ups and downs of preposition error detection in ESL writing
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Native judgments of non-native usage: experiments in preposition error detection
HumanJudge '08 Proceedings of the Workshop on Human Judgements in Computational Linguistics
Detection of grammatical errors involving prepositions
SigSem '07 Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions
Automatically acquiring models of preposition use
SigSem '07 Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions
Web-scale N-gram models for lexical disambiguation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
The role of PP attachment in preposition generation
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Generating confusion sets for context-sensitive error correction
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Algorithm selection and model adaptation for ESL correction tasks
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Data-driven correction of function words in non-native English
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Detection and correction of preposition and determiner errors in English: HOO 2012
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Informing determiner and preposition error correction with word clusters
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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Prepositions in English are a well-known challenge for language learners, and the computational analysis of preposition usage has attracted significant attention. Such research generally starts out by developing models of preposition usage for native English based on a range of features, from shallow surface evidence to deep linguistically-informed properties. While we agree that ultimately a combination of shallow and deep features is needed to balance the preciseness of exemplars with the usefulness of generalizations to avoid data sparsity, in this paper we explore the limits of a purely surface-based prediction of prepositions. Using a web-as-corpus approach, we investigate the classification based solely on the relative number of occurrences for target n-grams varying in preposition usage. We show that such a surface-based approach is competitive with the published state-of-the-art results relying on complex feature sets. Where enough data is available, in a surprising number of cases it thus is possible to obtain sufficient information from the relatively narrow window of context provided by n-grams which are small enough to frequently occur but large enough to contain enough predictive information about preposition usage.