Towards SMS Spam Filtering: Results under a New Dataset
By Tiago A. Almeida, José María Gómez Hidalgo, Tiago P. Silva. 2013
111 citations recorded by the source corpus.
Subfields: Natural Language Processing, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Ruiqi Zhong
- Grigorios Tsoumakas
- Zheng Zhang
- Neil Houlsby
- Catherine Wong
- Zoe Chrysopoulou
- Stamatis Karlos
- Kristy Lee
In Evaluation & Benchmarks
- Zoe Chrysopoulou
- Stamatis Karlos
- Ioannis Mollas
- Benjamin Stoddard
- Catherine Wong
- Felix Naumann
- Kristy Lee
- Yifeng Lu
Citations in the corpus
- Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections (2021, background): “-Becky" SMS Spam Detection (Almeida et al., 2013) This task detects whether the SMS is a spam message.”
- Transfer Learning with Neural AutoML (2018, methodology): “[38] use classical ML classifiers (Logistic Regression, SVMs, etc.”
- Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, methodology): “A dataset of 5574 SMS messages [170] was used to support the argument that it is equally important to select the appropriate model for the task at hand in addition to concentrating on making complex models understandable.”
- Explainable AI under contract and tort law: legal incentives and technical challenges (2020, result): “Our results for SMS-spam classification, which are similar to the ones reported by Almeida et al. (2013), are shown in Table 3.”
- Meta-tuning Language Models to Answer Prompts Better (2021, background): “SMS Spam Detection (Almeida et al., 2013) This task detects whether the SMS is a spam message.”
- Differentially Private Algorithms for Empirical Machine Learning (2014, background): “Differentially PrivateEmpirical Risk Minimization.”
- ETHOS: a multi-label hate speech detection dataset (2020, background): “Afterwards, a set of experiments are taking place in order to find a baseline performance of this specific dataset using a variety of state-of-the-art techniques, ranging from conventional ML algorithms and ensemble learners to Deep Neural Networks (DNNs) with and without embeddings information, in ”