Analyzing model behavior under different conditions and perturbations What are the interpretability challenges with complex text classification models?

Author(s): ROSARIO, RYAN ROBERT | Advisor(s): Wu, Yingnian | Abstract: Text classification typically performs best with large training sets, but short texts ... In this paper, we bridge this striking gap by presenting TextShield, a new adversarial defense framework specifically designed for Chinese-based DLTC models. The text classification algorithm takes a text string as input and outputs a probability for each of the class labels. Training datasets must be in CSV format. Measuring and Mitigating Unintended Bias in Text Classification · Abstract · Research Areas. Natural Language Processing · Responsible AI. Meet the teams ... Named Entity Recognition (NER) and Text Classification are powerful natural language processing (NLP) techniques that have unlocked immense value in various ... These techniques use for the construction of classification models that classify the text of autopsy reports. Data sets collected from these types will be ... Mar 18, 2025 ... Text classification is an AI technique that allows you to use an algorithm to sort text data into categories. This technology is an important ... Jun 5, 2023 ... In this blog post, we'll present seven powerful text classification techniques to fit all these situations. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets. Custom text classification. Custom named entity recognition (available in containers), $5. Text analytics for health (available in containers), 0M-0M - Included ...