Collocations such as heavy rain or make [a] decision are combinations of two elements where one (the base) is freely chosen, while the choice of the other (collocate) is restricted by the base. Research has consistently shown that collocations present difficulties even to the most advanced language learners, so that computational tools aimed at supporting them in the process of language learning can be of great value. However, in contrast to grammatical error detection and correction, collocation error marking and correction has not yet received the attention it deserves. This is unsurprising, considering the lack of existing collocation resources, in particular those that capture the different types of collocation errors, and the high cost of a manual creation of such resources. In this paper, we present an algorithm for the automatic generation of an artificial collocation error corpus of American English learners of Spanish that includes 17 different types of collocation errors and that can be used for automatic detection and classification of collocation errors in the writings of Spanish language learners.
@InProceedings{RODRÍGUEZ-FERNÁNDEZ18.970, author = {Sara Rodríguez-Fernández and Roberto Carlini and Leo Wanner}, title = "{Generation of a Spanish Artificial Collocation Error Corpus}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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