Attention is Not Always What You Need: Towards Efficient Classification of Domain-Specific Text

no code implementations • 31 Mar 2023 • Yasmen Wahba, Nazim Madhavji, John Steinbacher

The findings of this study show that for domain-specific TC tasks, a linear model can provide a comparable, cheap, reproducible, and interpretable alternative to attention-based models.

Question Answering Sentiment Analysis +2

A Comparison of SVM against Pre-trained Language Models (PLMs) for Text Classification Tasks

no code implementations • 4 Nov 2022 • Yasmen Wahba, Nazim Madhavji, John Steinbacher

In this paper, we compare the performance of four different PLMs on three public domain-free datasets and a real-world dataset containing domain-specific words, against a simple SVM linear classifier with TFIDF vectorized text.

Feature Engineering text-classification +1

Anomaly Detection in a Large-scale Cloud Platform

no code implementations • 21 Oct 2020 • Mohammad Saiful Islam, William Pourmajidi, Lei Zhang, John Steinbacher, Tony Erwin, Andriy Miranskyy

Cloud computing is ubiquitous: more and more companies are moving the workloads into the Cloud.

Anomaly Detection Cloud Computing