Term Based Semantic Clusters for Very Short Text Classification

Jasper Paalman, Shantanu Mullick, Kalliopi Zervanou, Yingqian Zhang


Abstract
Very short texts, such as tweets and invoices, present challenges in classification. Although term occurrences are strong indicators of content, in very short texts, the sparsity of these texts makes it difficult to capture important semantic relationships. 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, the Term Based Semantic Clusters (TBSeC) that employs terms to create distinctive semantic concept clusters. These clusters are ranked using a semantic similarity function which in turn defines a semantic feature space that can be used for text classification. Our method is evaluated in an invoice classification task. Compared to well-known content representation methods the proposed method performs competitively.
Anthology ID:
R19-1102
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
878–887
Language:
URL:
https://www.aclweb.org/anthology/R19-1102
DOI:
10.26615/978-954-452-056-4_102
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/R19-1102.pdf