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Found 10 papers, 3 papers with code
Crawling Under-Resourced Languages - a Portal for Community-Contributed Corpus Collection
The “Web as corpus” paradigm opens opportunities for enhancing the current state of language resources for endangered and under-resourced languages.
Have LLMs Made Active Learning Obsolete? Surveying the NLP Community
Supervised learning relies on annotated data, which is expensive to obtain.
Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language Models
Active learning is an iterative labeling process that is used to obtain a small labeled subset, despite the absence of labeled data, thereby enabling to train a model for supervised tasks such as text classification.
The Infinite Index: Information Retrieval on Generative Text-To-Image Models
Conditional generative models such as DALL-E and Stable Diffusion generate images based on a user-defined text, the prompt.
Trigger Warnings: Bootstrapping a Violence Detector for FanFiction
We present the first dataset and evaluation results on a newly defined computational task of trigger warning assignment.
The Impact of Main Content Extraction on Near-Duplicate Detection
Commercial web search engines employ near-duplicate detection to ensure that users see each relevant result only once, albeit the underlying web crawls typically include (near-)duplicates of many web pages.
Small-Text: Active Learning for Text Classification in Python
We introduce small-text, an easy-to-use active learning library, which offers pool-based active learning for single- and multi-label text classification in Python.
Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers
Active learning is the iterative construction of a classification model through targeted labeling, enabling significant labeling cost savings.
Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning
In total, we process nine categories and actively learn their representation in our dataset.
A Survey of Active Learning for Text Classification using Deep Neural Networks
We review AL for text classification using deep neural networks (DNNs) and elaborate on two main causes which used to hinder the adoption: (a) the inability of NNs to provide reliable uncertainty estimates, on which the most commonly used query strategies rely, and (b) the challenge of training DNNs on small data.
