Abstract
In this study, computation of compact document vectors by utilizing both terms and termsets for binary text categorization is addressed. In general, termsets are concatenated with all terms, leading to large document vectors. Selection of a subset of terms and termsets for compact but also effective representation of documents is considered in this study. Two different methods are studied for this purpose. In the first method, combination of terms and termsets in different proportions is evaluated. As an alternative approach, normalized ranking scores of terms and termsets are employed for subset selection. Experiments conducted on two widely used datasets have shown that termsets can effectively complement terms also in cases when small number of features are used to represent documents.
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Badawi, D., Altınçay, H. (2018). Compact Representation of Documents Using Terms and Termsets. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_7
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DOI: https://doi.org/10.1007/978-3-319-96136-1_7
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