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- W198476337 abstract "In the present work we consider the problem of narrow-domain clustering of short texts, such as academic abstracts. Our main objective is to check whether it is possible to improve the quality of k-means algorithm expanding the feature space by adding a dictionary of word groups that were selected from texts on the basis of a fixed set of patterns. Also, we check the possibility to increase the quality of clustering by mapping the feature spaces to a semantic space with a lower dimensionality using Latent Semantic Indexing (LSI). The results allow us to assume that the aforementioned modifications are feasible in practical terms as compared to the use of k-means in the feature space defined only by the main dictionary of the corpus." @default.
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- W198476337 date "2014-01-01" @default.
- W198476337 modified "2023-09-27" @default.
- W198476337 title "Clustering Narrow-Domain Short Texts Using K-Means, Linguistic Patterns and LSI" @default.
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- W198476337 doi "https://doi.org/10.1007/978-3-319-12580-0_18" @default.
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