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Found 14 papers, 2 papers with code
Press Freedom Monitor: Detection of Reported Press and Media Freedom Violations in Twitter and News Articles
Freedom of the press and media is of vital importance for democratically organised states and open societies.
Using Language Models on Low-end Hardware
This paper evaluates the viability of using fixed language models for training text classification networks on low-end hardware.
Using Text Classification with a Bayesian Correction for Estimating Overreporting in the Creditor Reporting System on Climate Adaptation Finance
To estimate realistic rates of overreporting in large data sets over times, we propose an approach based on state-of-the-art text classification.
Application of the interactive Leipzig Corpus Miner as a generic research platform for the use in the social sciences
This article introduces to the interactive Leipzig Corpus Miner (iLCM) - a newly released, open-source software to perform automatic content analysis.
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.
iLCM - A Virtual Research Infrastructure for Large-Scale Qualitative Data
The iLCM project pursues the development of an integrated research environment for the analysis of structured and unstructured data in a "Software as a Service" architecture (SaaS).
Detecting and assessing contextual change in diachronic text documents using context volatility
The new measure of context volatility that we propose models the degree by which terms change context in a text collection over time.
Modeling the dynamics of domain specific terminology in diachronic corpora
In terminology work, natural language processing, and digital humanities, several studies address the analysis of variations in context and meaning of terms in order to detect semantic change and the evolution of terms.
Leipzig Corpus Miner - A Text Mining Infrastructure for Qualitative Data Analysis
This paper presents the "Leipzig Corpus Miner", a technical infrastructure for supporting qualitative and quantitative content analysis.
PACE Corpus: a multilingual corpus of Polarity-annotated textual data from the domains Automotive and CEllphone
In this paper, we describe a publicly available multilingual evaluation corpus for phrase-level Sentiment Analysis that can be used to evaluate real world applications in an industrial context.
