Competition for skilled NLP professionals What are some resources for staying updated on the latest trends in text classification?

Abstract. Bidirectional Encoder Representations from Transformers (BERT) and BERT-based approaches are the current state-of-the-art in many natural language ... Text classification also known as text tagging or text categorization is the process of assigning tags/labels to unstructured text. Using Natural Language ... Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III. 2015. Deep Unordered Composition Rivals Syntactic Methods for Text Classification. In ... 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 ... Text classification models learn to assign one or more labels to text. You can use text classification over short pieces of text like sentences or headlines. In this module, we will learn about the different types of machine learning that exist and the operational steps of building a supervised machine learning ... The documents to be classified may be texts, images, music, etc. Each kind of document possesses its special classification problems. When not otherwise ... Feb 20, 2024 ... Prompt-based learning has demonstrated remarkable success in few-shot text classification, outperforming the traditional fine-tuning ... Jan 11, 2024 ... Criteria2Query is a NLP tool aiming to convert free-text eligibility criteria for a clinical trial to a structured query to aid in the ... May 25, 2016 ... Title:Adversarial Training Methods for Semi-Supervised Text Classification ... Abstract:Adversarial training provides a means of regularizing ...