Retrieving collocations from text: Xtract
Computational Linguistics - Special issue on using large corpora: I
An unsupervised method for detecting grammatical errors
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
TANGO: bilingual collocational concordancer
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Correcting ESL errors using phrasal SMT techniques
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A classifier-based approach to preposition and determiner error correction in L2 English
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A method for unsupervised broad-coverage lexical error detection and correction
EdAppsNLP '09 Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
Using parse features for preposition selection and error detection
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
StringNet as a computational resource for discovering and investigating linguistic constructions
EUCCL '10 Proceedings of the NAACL HLT Workshop on Extracting and Using Constructions in Computational Linguistics
Search right and thou shalt find...: using web queries for learner error detection
IUNLPBEA '10 Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications
TransAhead: a writing assistant for CAT and CALL
EACL '12 Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics
TransAhead: a computer-assisted translation and writing tool
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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We introduce a method for learning to describe the attendant contexts of a given query for language learning. In our approach, we display phraseological information in the form of a summary of general patterns as well as lexical bundles anchored at the query. The method involves syntactical analyses and inverted file construction. At run-time, grammatical constructions and their lexical instantiations characterizing the usage of the given query are generated and displayed, aimed at improving learners' deep vocabulary knowledge. We present a prototype system, GRASP, that applies the proposed method for enhanced collocation learning. Preliminary experiments show that language learners benefit more from GRASP than conventional dictionary lookup. In addition, the information produced by GRASP is potentially useful information for automatic or manual editing process.