6 papers · 1 filter
Word-Centered Semantic Graphs for Interpretable Diachronic Sense Tracking
Imene Kolli, Kai-Robin Lange, Jonas Rieger +1
We propose an interpretable, graph-based framework for analyzing semantic shift in diachronic corpora. For each target word and time slice, we induce a word-centered semantic netwo…
Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
Kai-Robin Lange, Tobias Schmidt, Matthias Reccius +3
With rapidly evolving media narratives, it has become increasingly critical to not just extract narratives from a given corpus but rather investigate, how they develop over time. W…
ttta: Tools for Temporal Text Analysis
Kai-Robin Lange, Niklas Benner, Lars Grönberg +4
Text data is inherently temporal. The meaning of words and phrases changes over time, and the context in which they are used is constantly evolving. This is not just true for socia…
Zeitenwenden: Detecting changes in the German political discourse
Kai-Robin Lange, Jonas Rieger, Niklas Benner +1
From a monarchy to a democracy, to a dictatorship and back to a democracy -- the German political landscape has been constantly changing ever since the first German national state…
SpeakGer: A meta-data enriched speech corpus of German state and federal parliaments
Kai-Robin Lange, Carsten Jentsch
The application of natural language processing on political texts as well as speeches has become increasingly relevant in political sciences due to the ability to analyze large tex…
Lex2Sent: A bagging approach to unsupervised sentiment analysis
Kai-Robin Lange, Jonas Rieger, Carsten Jentsch
Unsupervised text classification, with its most common form being sentiment analysis, used to be performed by counting words in a text that were stored in a lexicon, which assigns…