Traditionally, the process whereby a lexicographer identifies a lexical item to add to a dictionary -- a database of lexical items -- has been time-consuming and subjective. In the modern age of online dictionaries, all queries for lexical entries not currently in the database are indistinguishable from a larger list of misspellings, meaning that potential new or trending entries can get lost easily. In this project, we develop a system that uses machine learning techniques to assign these ``misspells'' a probability of being a novel or missing entry, incorporating signals from orthography, usage by trusted online sources, and dictionary query patterns.
@InProceedings{BROAD18.222, author = {Claire Broad and Helen Langone and David Guy Brizan}, title = "{Candidate Ranking for Maintenance of an Online Dictionary}", 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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