| Author(s): |
de Sousa, Gustavo Jose
;
Guimaraes Pedronette, Daniel Carlos
;
Papa, Joao Paulo
;
Guilherme, Ivan Rizzo
Total Authors: 4
|
| Document type: | Journal article |
| Source: | FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, ACL-IJCNLP 2021; v. N/A, p. 9-pg., 2021-01-01. |
| Abstract | |
The drastic decrease in mobile SMS costs turned phone users more prone to spam messages, usually with unwanted marketing or questionable content. As such, researchers have proposed different methods for detecting SMS spam messages. This paper presents a technique for embedding SMS messages into vector spaces that is suitable for spam detection. The proposed approach relies on mining patterns that are relevant for distinguishing spam from legitimate messages. A subset of those patterns is used to construct a function that maps text messages into a multidimensional vector space. The extracted patterns are represented as skip-grams of token attributes, where a skip-gram can be seen as a generalization of the n-gram model that allows a distance greater than one between matched tokens in the text. We evaluate the proposed approach using the generated vectors for spam classification on the UCI Spam Collection dataset. The experiments showed that our method combined with shallow networks reached accuracy that is competitive with state-of-the-art approaches. (AU) | |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |
| FAPESP's process: | 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks |
| Grantee: | Daniel Carlos Guimarães Pedronette |
| Support Opportunities: | Research Grants - Young Investigators Grants - Phase 2 |