This paper describes a method for distinguishing lexical layers in environmental corpora (i.e. the general lexicon, the transdisciplinary lexicon and two sets of lexical items related to the domain). More specifically we aim to identify the general environmental lexicon (GEL) and assess the extent to which we can set it apart from the others. The general intuition on which this research is based is that the GEL is both well-distributed in a specialized corpus (criterion 1) and specific to this type of corpora (criterion 2). The corpus used in the current experiment, made of 6 subcorpora that amount to 4.6 tokens, was compiled manually by terminologists for different projects designed to enrich a terminological resource. In order to meet criterion 1, the distribution of the GEL candidates is evaluated using a simple and well-known measure called. As for criterion 2, GEL candidates are extracted using a term extractor, which provides a measure of their specificity relative to a corpus. Our study focuses on single-word lexical items including nouns, verbs and adjectives. The results were validated by a team of 4 annotators who are all familiar with the environmental lexicon and they show that using a high specificity threshold and a low idf threshold constitutes a good starting point to identify the GEL layer in our corpora.
@InProceedings{DROUIN18.752, author = {Patrick Drouin and Marie-Claude L'Homme and Benoît Robichaud}, title = "{Lexical Profiling of Environmental Corpora}", 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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