Term ranking adaptation to the domain: genetic algorithm based optimisation of the C-Value
Résumé
Approaches based on linguistic rules have been proposed to automatically extract candidate terms to help the terminology building from corpora. However, they face to the difficulty to identify the relevant terms among the noun phrases extracted. Although several statistical measures as the frequency or the C-Value have been proposed to ranked the terms according to their termhood, they fail to propose corpus and domain-independent ranking. We tackle this problem by proposing a parametrised C-Value which optimally considers the length and the syntactic roles of the nested terms thanks to a genetic algorithm. We compare its impact on the ranking of term extracted from on three corpora. Results show average precision increases by 9% above the frequency based ranking and by 12% above the C-Value based ranking.