Sense-Tagging at the Cycle-Level Using GLDB
ResuméThis report describes a large-scale attempt to identify automatically the appropriate sense for content words taken from Swedish open-source texts. Sense-tagging, 'the process of assigning the appropriate sense from some kind of lexicon to the (content) words in a text', is a difficult and demanding task in Natural Language Processing and researchers have been engaged in finding a suitable solution to the problem for a very long time. The usefulness of automatically assigning each word in unrestricted text with its most likely sense is necessary for a great spectrum of applications. The sense-tagger described here has been tested both on a random sample of content words, as well as on a large population of a single ambiguous entry. In the first case, the achieved precision was 84,21 %, and in the second 82,75% respectively. Evaluation was made against manually sense-annotated texts.
Kokkinakis, D., & Kokkinakis, S. J. (2001). Sense-Tagging at the Cycle-Level Using GLDB. Nordiske Studier I Leksikografi, (5). Hentet fra https://tidsskrift.dk/nsil/article/view/19460
Nordisk Forening for Leksikografi/NSL og forfatterne.