activity
20172022
most citedA Survey of Active Learning for Text Classification using Deep Neural Networks

61 citations · 86 across the 7 of their papers we have counts for

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5 papers · 1 filter

cs.CL20211 cited

Application of the interactive Leipzig Corpus Miner as a generic research platform for the use in the social sciences

Christian Kahmann, Andreas Niekler, Gregor Wiedemann

This article introduces to the interactive Leipzig Corpus Miner (iLCM) - a newly released, open-source software to perform automatic content analysis. Since the iLCM is based on th…

cs.CL202061 cited

A Survey of Active Learning for Text Classification using Deep Neural Networks

Christopher Schröder, Andreas Niekler

Natural language processing (NLP) and neural networks (NNs) have both undergone significant changes in recent years. For active learning (AL) purposes, NNs are, however, less commo…

cs.CL2017

Detecting and assessing contextual change in diachronic text documents using context volatility

Christian Kahmann, Andreas Niekler, Gerhard Heyer

Terms in diachronic text corpora may exhibit a high degree of semantic dynamics that is only partially captured by the common notion of semantic change. The new measure of context…

cs.CL20175 cited

Modeling the dynamics of domain specific terminology in diachronic corpora

Gerhard Heyer, Cathleen Kantner, Andreas Niekler +2

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 sema…

cs.CL201712 cited

Leipzig Corpus Miner - A Text Mining Infrastructure for Qualitative Data Analysis

Andreas Niekler, Gregor Wiedemann, Gerhard Heyer

This paper presents the "Leipzig Corpus Miner", a technical infrastructure for supporting qualitative and quantitative content analysis. The infrastructure aims at the integration…