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.
It would in principle be possible to use an unsupervised approach and attempt to disambiguate synonyms on the basis of raw corpora. Such approaches however are not based on a fixed list of senses where the senses for a target word are a closed list coming from a dictionary. Instead they induce word senses directly from the corpus by using clustering techniques, which group together similar examples. To associate synonyms with definitions, it would therefore be necessary to define an additional mapping between corpus induced word senses and dictionary definitions. As noted in @cite_14 , such a mapping usually introduces noise and information loss however.
@cite_14
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Veronis (2004) has recently proposed an innovative unsupervised algorithm for word sense disambiguation based on small-world graphs called HyperLex. This paper explores two sides of the algorithm. First, we extend Veronis' work by optimizing the free parameters (on a set of words which is different to the target set). Second, given that the empirical comparison among unsupervised systems (and with respect to supervised systems) is seldom made, we used hand-tagged corpora to map the induced senses to a standard lexicon (WordNet) and a publicly available gold standard (Senseval 3 English Lexical Sample). Our results for nouns show that thanks to the optimization of parameters and the mapping method, HyperLex obtains results close to supervised systems using the same kind of bag-of-words features. Given the information loss inherent in any mapping step and the fact that the parameters were tuned for another set of words, these are very interesting results.