Difficulties in handling negations and sarcasm Dealing with evolving language and topics

This example shows how to incrementally train a model to classify documents based on word frequencies in the documents; a bag-of-words model. Dec 24, 2018 ... This workflow demonstrates how tidy data principles can be used not just for data cleaning and munging, but for sophisticated machine learning as well. Feb 20, 2024 ... Prompt-based learning has demonstrated remarkable success in few-shot text classification, outperforming the traditional fine-tuning ... This paper aims to extensively examine the impact of preprocessing on text classification in terms of various aspects such as classification accuracy, text ... To help middle school students explore Artificial Intelligence (AI), we built a text classifier extension into a block-based programming interface using ... Existing statistical text learning algorithms can be trained to approximately classify documents, given a sufficient set of labeled training examples. These ... SST-2 Binary text classification with XLM-RoBERTa model. Author: Parmeet Bhatia. Overview. This tutorial demonstrates how to train a text classifier on ... In natural language processing (NLP), text classification tasks are increasingly fine-grained, as datasets are fragmented into a larger number of classes ... Apr 3, 2025 ... Text classification is a key natural language processing task widely used in areas such as news classification, sentiment analysis, topic ... May 13, 2021 ... I have a few text classifiers that i am testing out, and they work well for data that does fit into any predefined category.