Contributions to the study of SMS spam filtering: new collection and results

By Tiago A. Almeida, José María Gómez Hidalgo, Akebo Yamakami. 2011

619 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP (2021, background): “…qa Welbl et al. 2017 scitail cls/nli Khot et al. 2018 search_qa qa/closed-book qa Dunn et al. 2017 sick cls/nli Marelli et al. 2014 sms_spam cls/other Almeida et al. 2011 social_i_qa qa/multiple-choice qa Sap et al. 2019 spider cg/other Yu et al. 2018 squad-no_context qa/closed-book qa Rajpurkar…”
Learning from Rules Generalizing Labeled Exemplars (2020, methodology): “(Almeida et al., 2011): This dataset contains 5.5k text messages labeled as spam/not-spam, out of which 500 were held out for validation and 500 for testing.”
Towards SMS Spam Filtering: Results under a New Dataset (2013, methodology): “They have carried out experiments on the SMS Spam Collection [1] and compared the results with the ones achieved by the well-known CLUTO framework.”
Toward a Quantitative Survey of Dimension Reduction Techniques (2019, methodology): “Text dataset created for sentiment analysis; SMS Spam Collection (sms) [48].”
Transfer Automatic Machine Learning (2018, methodology): “Almeida et al. (2013) use classical ML classifiers (Logistic Regression, SVMs, etc.”
Formalizing and Benchmarking Prompt Injection Attacks and Defenses (2023, methodology): “…we collect some clean data records; use an LLM to calculate the model-dependent perplexity for each clean data record; and we select a threshold such that the False Positive Rate , i.e., fraction of the clean data records falsely detected as compromised, is no larger than a given value (e.g., 1%).”