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 language | English |
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Number of pages | 12 |
Journal | CEUR Workshop Proceedings |
Volume | 2491 |
Publication status | Published - Nov 2019 |
Externally published | Yes |
Event | 31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019 - Brussels, Belgium Duration: 6 Nov 2019 → 8 Nov 2019 http://ceur-ws.org/Vol-2491/ |
Bibliographical note
Publisher Copyright:© 2019 for this paper by its authors.
ASJC Scopus subject areas
- General Computer Science