Never Retreat, Never Retract: Argumentation Analysis for Political Speeches
Authors: Stefano Menini, Elena Cabrio, Sara Tonelli, Serena Villata
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test and discuss our approach based on the analysis of documents issued by R. Nixon and J. F. Kennedy during 1960 presidential campaign. We rely on a supervised classifier to predict argument relations (i.e., support and attack), obtaining an accuracy of 0.72 on a dataset of 1,462 argument pairs. |
| Researcher Affiliation | Academia | Stefano Menini,1,3 Elena Cabrio,2 Sara Tonelli,1 Serena Villata2 1Fondazione Bruno Kessler, Trento, Italy 2 Universit e Cˆote d Azur, CNRS, Inria, I3S, France 3University of Trento, Italy |
| Pseudocode | No | The paper describes the system architecture and features but does not include any pseudocode or explicitly labeled algorithm blocks. |
| Open Source Code | No | The paper mentions that 'The script and the argumentation graphs... are available at https://dh.fbk.eu/resources/political-argumentation' but this refers to a script for converting data and the output graphs, not the full source code for the SVM model or feature extraction methodology. |
| Open Datasets | Yes | The dataset is available at https://dh.fbk.eu/resources/political-argumentation |
| Dataset Splits | Yes | We test the performance of the classification pipeline using the 1,462 manually annotated pairs with 10-fold cross-validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions software tools like 'Stanford Core NLP suite', 'LIBSVM', 'word2vec', and 'Excitement Open Platform', but does not provide specific version numbers for any of them. |
| Experiment Setup | No | The paper describes the features used and the choice of SVM with a radial kernel, but it does not specify concrete hyperparameter values (e.g., C, gamma for SVM) or other system-level training settings needed for full reproducibility. |