4. Text Classification

Text Classification: What It Is & How to Get Started - Levity AI

Text classification is a Machine Learning approach for automatically categorizing open-ended text into a number of predetermined categories.

Text Classification is Your New Secret Weapon | by Adam Geitgey

A lot of websites use text classification as a first-line defense against abusive users. By also taking the model's confidence score into ...

4. Text Classification - Practical Natural Language Processing [Book]

A comparison of several ensemble methods for text categorization. IEEE International Conference on Services Computing (2004): 419–422.

9 Text Classification Examples in Action - Levity AI

1. Use text classification for social listening · 2. Categorizing customer support tickets · 3. Customer feedback sentiment analysis · 4. Product ...

What is Text Classification? - Elastic

Types of text classification you might encounter include: Text sentiment analysis determines the sentiment or emotion expressed in a piece of text, usually ...

How to use GPT-4 and OpenAI's functions for text classification

We share a technical guide on how we used OpenAI's GPT-4 and function calling to achieve this. This approach is very general and can be used to classify texts.

Best Model for Text Classification: Gemini Pro, GPT-4 or Claude2?

We compared four models to see which one is the best at figuring out if a customer support ticket has been resolved or not. In our experiment, Gemini Pro ...

7 Text Classification Techniques for Any Scenario - Dataiku Blog

In this blog post, we'll present seven powerful text classification techniques to fit all these situations.

What is Text Classification? - Hugging Face

Text Classification is the task of assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing ...

Text Classification in Python: A Complete Guide | Analytics Vidhya

Text Classification is an example of supervised machine learning task since a labelled dataset containing text documents and their labels is ...

Text Classification: Best Practices for Real World Applications

In this article, I will highlight some of the best practices in building text classifiers that actually work for real world scenarios.

Text Classification | Papers With Code

Text Classification problems include emotion classification, news classification, citation intent classification, among others. Benchmark datasets for ...

Text Classification: A Review of Deep learning Methods - arXiv

This paper introduces deep learning-based text classification algorithms, including important steps required for text classification tasks such ...

Text Classification - Lena Voita

Text classifiers are often used not as an individual task, but as part of bigger pipelines. For example, a voice assistant classifies your utterance to ...

[P] Text classification model with a large number of classes - Reddit

This sub-area is typically referred as Extreme Classification which targets multiple real world applications including product categorisation, ...

Text Classification · Prodigy · An annotation tool for AI, Machine ...

Prodigy provides several sorter functions that take a stream of (score, example) tuples and pick examples to send out for annotation. The textcat.teach recipe ...

Text Classification model — NVIDIA NeMo Framework User Guide

Text Classification is a sequence classification model based on BERT-based encoders. It can be used for a variety of tasks like text classification, sentiment ...

Text Classification - an overview | ScienceDirect Topics

Text classification is a powerful tool for improving the efficiency and utility of processing text data. If the documents and the class labels are properly ...

Understanding Text Classification in Python | DataCamp

The goal of text classification is to categorize or predict a class of unseen text documents, often with the help of supervised machine learning ...

How to create Training data for Text classification on 4 categories

My idea is to classify sentences from the data into one of these categories: Risk, Opportunity and Irrelevant (no risk, no opportunity, default categorie).