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).
|Number of pages||12|
|Publication status||Published - 8 Nov 2019|
|Event||31st Benelux Conference on Artificial Intelligence and 28th Belgian-Dutch Conference on Machine Learning, BNAIC/BeneLearn 2019 - Brussels, Belgium|
Duration: 6 Nov 2019 → 8 Nov 2019
|Conference||31st Benelux Conference on Artificial Intelligence and 28th Belgian-Dutch Conference on Machine Learning, BNAIC/BeneLearn 2019|
|Abbreviated title||BNAIC 2019|
|Period||6/11/19 → 8/11/19|