Clustering Narrow-Domain Short Texts Using K-Means, Linguistic Patterns and LSI

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.