Term based semantic clusters for very short text classification

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Abstract

Very short texts, such as tweets and invoices, present challenges in classification. Such texts abound in ellipsis, grammatical errors, misspellings, and semantic variation. Although term occurrences are strong indicators of content, in very short texts, sparsity makes it difficult to capture enough content for a semantic classifier A solution calls for a method that not only considers term occurrence, but also handles sparseness well. In this work, we introduce such an approach for the classification of short invoice descriptions, in such a way that each class reflects a different group of products or services. The developed algorithm is called Term Based Semantic Clusters (TBSeC).

Original languageEnglish
Number of pages12
Publication statusPublished - 8 Nov 2019
Event31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019 - Brussels, Belgium
Duration: 6 Nov 20198 Nov 2019

Conference

Conference31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019
CountryBelgium
CityBrussels
Period6/11/198/11/19

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    Paalman, J., Mullick, S., Zervanou, K., & Zhang, Y. (2019). Term based semantic clusters for very short text classification. Abstract from 31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019, Brussels, Belgium.