Exploring Word Sense Distribution in Ukrainian with a Semantic Vector Space Model
Nataliia Cheilytko | Ruprecht von Waldenfels
Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)
The paper discusses a Semantic Vector Space Model targeted at revealing how Ukrainian word senses vary and relate to each other. One of the benefits of the proposed semantic model is that it considers second-order context of the words and, thus, has more potential to compare and distinguish word senses observed in a unique concordance line. Combined with visualization techniques, this model makes it possible for a lexicographer to explore the Ukrainian word senses distribution on a large-scale. The paper describes the first results of the research performed and the following steps of the initiative.