Visualizing and understanding the decision-making process of text classification models

Abstract. Bidirectional Encoder Representations from Transformers (BERT) and BERT-based approaches are the current state-of-the-art in many natural language ... 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. This example shows how to incrementally train a model to classify documents based on word frequencies in the documents; a bag-of-words model. Feb 28, 2024 ... Text Classification. Text classification analyses the text and identifies categories for the content with a confidence score. Text ... Feb 19, 2021 ... Text classification to streamline online wildlife trade analyses. This is a Preprint and has not been peer reviewed. The published version of ... Use the text classification models to classify a set of words from a resource based on three text classification algorithms: Bag-of-Words, N-Gram, and Text ... Jul 18, 2022 ... Text classification algorithms are at the heart of a variety of software systems that process text data at scale. Email software uses text ... Oct 28, 2013 ... Text categorization ... Text categorization (a.k.a. text classification) is the task of assigning predefined categories to free-text documents. It ... Jul 17, 2019 ... In this work, we propose to use graph convolutional networks for text classification. We build a single text graph for a corpus based on word co ... May 29, 2012 ... ... text classification using a world knowledge ontology. Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 ...