@inproceedings{nazar-lindemann-2022-terminology,
    title = "Terminology extraction using co-occurrence patterns as predictors of semantic relevance",
    author = "Nazar, Rogelio  and
      Lindemann, David",
    editor = "Costa, Rute  and
      Carvalho, Sara  and
      Ani{\'c}, Ana Ostro{\v{s}}ki  and
      Khan, Anas Fahad",
    booktitle = "Proceedings of the Workshop on Terminology in the 21st century: many faces, many places",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.term-1.5/",
    pages = "26--29",
    abstract = "We propose a method for automatic term extraction based on a statistical measure that ranks term candidates according to their semantic relevance to a specialised domain. As a measure of relevance we use term co-occurrence, defined as the repeated instantiation of two terms in the same sentences, in indifferent order and at variable distances. In this way, term candidates are ranked higher if they show a tendency to co-occur with a selected group of other units, as opposed to those showing more uniform distributions. No external resources are needed for the application of the method, but performance improves when provided with a pre-existing term list. We present results of the application of this method to a Spanish-English Linguistics corpus, and the evaluation compares favourably with a standard method based on reference corpora."
}