Year: 2025
Type: conference-paper
Abstract: Being aware of frames, i. e., the aspect-based grouping of arguments, is crucial in applications that build upon a corpus of arguments, allowing, among others, biases and filter bubbles to be mitigated.However, manually identifying and naming these frames can be timeconsuming and therefore not feasible for larger datasets.Within this work, we present a sequential three-step pipeline for automating this task in a data-driven manner.After embedding the arguments, we apply clustering algorithms for identifying the frames and subsequently, utilize methods from the field of cluster labeling to name the frames.The proposed approach is tailored towards the requirements of practical applications where arguments may not be easily split into their argumentative units and hence can belong to more than one frame.Performing a component-wise evaluation, we determine the best-performing configuration of the pipeline.Our results indicate that frames should be identified by performing overlapping and not exclusive clustering and the naming of frames can be accomplished best by extracting aspect terms and weighting them with c-TF-IDF.
Institution: Universität Ulm
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W4412889284