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Yayın Integrating Turkish Wordnet KeNet to Princeton WordNet: The case of one-to-many correspondences(Institute of Electrical and Electronics Engineers Inc., 2019-10) Bakay, Özge; Ergelen, Özlem; Yıldız, Olcay TanerIn this paper, we introduce a novel approach of forming interlingual relations between multilingual wordnets. We have mapped Turkish senses in KeNet with their corresponding senses in Princeton WordNet by drawing one-To-many correspondences. As a result of language-specific properties, one synset in one language is matched with multiple synsets in the other language in some cases. Our method of integrating KeNet into a multilingual network also included mapping the most frequent 5000 senses in English with their equivalent senses in Turkish. What we demonstrate is that one-To-many interlingual correspondances are necessary to include in mappings both from Turkish-To-English and English-To-Turkish. Furthermore, one-To-many mappings give us insights into the semantic relations to be constructed in Turkish, such as hypernymy.Yayın Comparing sense categorization between English propbank and english wordnet(Oficyna Wydawnicza Politechniki Wroclawskiej, 2020) Bakay, Özge; Avar, Begüm; Yıldız, Olcay TanerGiven the fact that verbs play a crucial role in language comprehension, this paper presents a study which compares the verb senses in English PropBank with the ones in English WordNet through manual tagging. After analyzing 1554 senses in 1453 distinct verbs, we have found out that while the majority of the senses in PropBank have their one-to-one correspondents in WordNet, a substantial amount of them are differentiated. Furthermore, by analysing the differences between our manually-tagged and an automatically-tagged resource, we claim that manual tagging can help provide better results in sense annotation.Yayın Problems caused by semantic drift in WordNet synset construction(Institute of Electrical and Electronics Engineers Inc., 2019-09) Bakay, Özge; Ergelen, Özlem; Yıldız, Olcay TanerIn this study, we summarize the semantic drift problem that occur in specific synsets of KeNet, a Turkish WordNet, which is caused by mis-merging of semantically-related lexical items, morphological markings and false part of speech (POS) matchings. We present our approach to these problems in order to eliminate the semantic drift. We have re-analyzed the dictionary definitions of the items, placed those that possess different verbal markings into separate synsets, and divided synsets based on the POS of the items in them.
