In this paper, we combine several NLP-functionalities to organize examples drawn from corpora. The application’s primary target audience are language learners. Currently, authentic linguistic examples for a given keyword search are often organized alphabetically according to context. From this, it is not always clear which contextual regularities actually exist on a syntactic, collocational and semantic level. Showing information at different levels of abstraction will help with the discovery of linguistic regularities and thus improve linguistic understanding. Practically this translates in a system that groups retrieved results on syntactic grounds, after which the examples are further organized at the hand of semantic similarity within certain phrasal slots. Visualization algorithms are then used to show focused information in phrasal slots, laying bare semantic restrictions within the construction.
@InProceedings{DE HERTOG18.729, author = {Dirk De Hertog and Piet Desmet}, title = "{Contextualized Usage-Based Material Selection}", 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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