Automated suggestions for miscollocations
EdAppsNLP '09 Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
The effects of learner errors on the development of a collocation detection tool
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Correcting semantic collocation errors with L1-induced paraphrases
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Towards advanced collocation error correction in Spanish learner corpora
Language Resources and Evaluation
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In recent years, collocation has been widely acknowledged as an essential characteristic to distinguish native speakers from non-native speakers. Research on academic writing has also shown that collocations are not only common but serve a particularly important discourse function within the academic community. In our study, we propose a machine learning approach to implementing an online collocation writing assistant. We use a data-driven classifier to provide collocation suggestions to improve word choices, based on the result of classification. The system generates and ranks suggestions to assist learners' collocation usages in their academic writing with satisfactory results.