Abstract
The use of semantics in tasks related to information retrieval has become, in recent years, a vast field of research. Considering supervised text classification, which is the main interest of this work, semantics can be involved at different steps of text processing: during indexing step, during training step and during class prediction step. As for class prediction step, new text-to-text semantic similarity measures can replace classical similarity measures that are traditionally used by some classification methods for decision-making. In this paper we propose a new measure for assessing semantic similarity between texts based on TF/IDF with a new function that aggregates semantic similarities between concepts representing the compared text documents pair-to-pair. Experimental results demonstrate that our measure outperforms other semantic and classical measures with significant improvements.
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Albitar, S., Fournier, S., Espinasse, B. (2014). An Effective TF/IDF-Based Text-to-Text Semantic Similarity Measure for Text Classification. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8786. Springer, Cham. https://doi.org/10.1007/978-3-319-11749-2_8
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DOI: https://doi.org/10.1007/978-3-319-11749-2_8
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