Acquisition and enrichment of morphological and morphosemantic knowledge from the French Wiktionary
Franck Sajous and
Proceedings of the
Workshop on Lexical and Grammatical Resources for Language Processing (LG-LP 2014)
Jorge Baptista, Pushpak Bhattacharyya, Christiane Fellbaum, Mikel Forcada, Chu-Ren Huang, Svetla Koeva, Cvetana Krstev, Eric Laporte
Association for Computational Linguistics and Dublin City University
[ PDF article ]
N. Hathout , F. Sajous and B. Calderone (2014).
Acquisition and enrichment of morphological and morphosemantic knowledge from the French Wiktionary.
Proceedings of the Workshop on Lexical and Grammatical Resources for Language Processing (LG-LP 2014),
pp. 65-74, Dublin, Ireland
[ .bib ]
We present two approaches to automatically acquire morphologically related words from Wiktionary.
Starting with related words explicitly mentioned in the dictionary, we propose a method
based on orthographic similarity to detect new derived words from the entries' definitions with
an overall accuracy of 93.5%. Using word pairs from the initial lexicon as patterns of formal
analogies to filter new derived words enables us to rise the accuracy up to 99%, while extending
the lexicon's size by 56%. In a last experiment, we show that it is possible to semantically type
the morphological definitions, focusing on the detection of process nominals.