SASA Dictionary as the Gold Standard for Good Dictionary Examples for Serbian

Abstract

In this paper we present a model for selection of good dictionary examples for Serbian and the development of initial model components. The method used is based on a thorough analysis of various lexical and syntactic features in a corpus compiled of examples from the five digitized volumes of the Serbian Academy of Sciences and Arts (SASA) dictionary. The initial set of features was inspired by a similar approach for other languages. The feature distribution of examples from this corpus is compared with the feature distribution of sentence samples extracted from corpora comprising various texts. The analysis showed that there is a group of features which are strong indicators that a sentence should not be used as an example. The remaining features, including detection of non-standard and other marked lexis from the SASA dictionary, are used for ranking. The selected candidate examples, represented as featurevectors, are used with the GDEX ranking tool for Serbian candidate examples and a supervised machine learning model for classification on standard and non-standard Serbian sentences, for further integration into a solution for present and future dictionary production projects.