We present a method for grouping the synonyms of a lemma according to its dictionary senses. The senses are defined by a large machine readable dictionary for French, the TLFi (Tr 'esor de la langue fran c aise informatis 'e) and the synonyms are given by 5 synonym dictionaries (also for French). To evaluate the proposed method, we manually constructed a gold standard where for each (word, definition) pair and given the set of synonyms defined for that word by the 5 synonym dictionaries, 4 lexicographers specified the set of synonyms they judge adequate. While inter-annotator agreement ranges on that task from 67 to at best 88 depending on the annotator pair and on the synonym dictionary being considered, the automatic procedure we propose scores a precision of 67 and a recall of 71 . The proposed method is compared with related work namely, word sense disambiguation, synonym lexicon acquisition and WordNet construction.
This paper describes automatic construction a freely-available wordnet for French (WOLF) based on Princeton WordNet (PWN) by using various multilingual resources. Polysemous words were dealt with an approach in which a parallel corpus for five languages was word-aligned and the extracted multilingual lexicon was disambiguated with the existing wordnets for these languages. On the other hand, a bilingual approach sufficed to acquire equivalents for monosemous words. Bilingual lexicons were extracted from Wikipedia and thesauri. The results obtained from each resource were merged and ranked according to the number of resources yielding the same literal. Automatic evaluation of the merged wordnet was performed with the French WordNet (FREWN). Manual evaluation was also carried out on a sample of the generated synsets. Precision shows that the presented approach has proved to be very promising and applications to use the created wordnet are already intended. This paper compares automatically generated sets of synonyms in French and Slovene wordnets with respect to the resources used in the construction process. Polysemous words were disambiguated via a five-language word-alignment of the SEERA.NET parallel corpus, a subcorpus of the JRC Acquis. The extracted multilingual lexicon was disambiguated with the existing wordnets for these languages. On the other hand, a bilingual approach sufficed to acquire equivalents for monosemous words. Bilingual lexicons were extracted from different resources, including Wikipedia, Wiktionary and EUROVOC thesaurus. A representative sample of the generated synsets was evaluated against the goldstandards.