In this paper we present an approach to support production of synsets for Serbian WordNet (SerWN) by adjusting Princeton WordNet (PWN) synsets using several bilingual English-Serbian resources. PWN synset definitions were automatically translated and post-edited, if needed, while candidate literals for Serbian synsets were obtained automatically from a list of translational equivalents compiled form bilingual resources. Preliminary results obtained from a set of 1248 selected PWN synsets show that the produced Serbian synsets contain 4024 literals, out of which 2278 were offered by the system we present in this paper, whereas experts added the remaining 1746. Approximately one half of synset definitions obtained automatically were accepted with no or minor corrections. These first results are encouraging, since the efficiency of synset production for SerWN was increased. There is also space for further improvement of this approach to wordnet enrichment.

The aim of this paper is to show a language-independent process of creating a new semantic relation between adjectives and nouns in wordnets. The existence of such a relation is expected to improve the detection of figurative language and sentiment analysis (SA). The proposed method uses an annotated corpus to explore the semantic knowledge contained in linguistic constructs performing as the rhetorical figure Simile. Based on the frequency of occurrence of similes in an annotated corpus, we propose a new relation, which connects the noun synset with the synset of an adjective representing that noun’s specific attribute. We elaborate on adding this new relation in the case of the Serbian WordNet (SWN). The proposed method is evaluated by human judgement in order to determine the relevance of automatically selected relation items. The evaluation has shown that 84% of the automatically selected and the most frequent linguistic constructs, whose frequency threshold was equal to 3, were also selected by humans.