Vector space models of kyiv city petitions
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Дата
2021
Автори
Науковий керівник
Назва журналу
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Назва тому
Видавець
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
Анотація
In this study, we explore and compare two ways of vector space model creation for Kyiv city petitions. Both models are built on top of word vectors based on the distributional hypothesis, namely Word2Vec and FastText. We train word vectors on the dataset of Kyiv city petitions, preprocess the documents, and apply averaging to create petition vectors. Visualizations of the vector spaces after dimensionality reduction via UMAP are demonstrated in an attempt to show their overall structure. We show that the resulting models can be used to effectively query semantically related petitions as well as search for clusters of related petitions. The advantages and disadvantages of both models are analyzed.
Опис
Ключові слова
vector space model, FastText, Word2Vec, petitions analysis, UMAP
Бібліографічний опис
Shaptala, R. Vector space models of kyiv city petitions / Shaptala Roman, Kyselov Gennadiy // Information, Computing and Intelligent systems. – 2021. – No. 2. – Pp. 26–34. – Bibliogr.: 19 ref.