Titlebook: Computational Linguistics and Intelligent Text Processing; 13th International C Alexander Gelbukh Conference proceedings 2012 Springer-Verl

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https://doi.org/10.1007/978-3-031-66913-2al information that could be swallowed up in the volume if represented and treated in a single whole context vector. We show that the empirical results obtained are competitive with the standard approach traditionally dedicated to this task.

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Clustering Short Text and Its Evaluations, Complete Link, Single Link, Average Link hierarchical clustering and Spectral clustering, and three evaluation methods, clustering F-measure, adjusted Rand Index, and V. Our experiments show that corpus based similarity measures do not significantly affect the clusters and that the performance of

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Computational Linguistics and Intelligent Text Processing13th International C

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978-3-642-28600-1Springer-Verlag Berlin Heidelberg 2012

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Natalia Maloshonok,Irina Shcheglova on an approach of an extensive set, over 650, linguistic features to represent the context of a word, and a range of machine learning approaches in the lexical choice task. We extend previous work by experimenting with unsupervised and semi-supervised methods, and use automatic feature selection to

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