Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Automated Narrative Information Extraction Using Non-Linear Pipelines
Authors: Josep Valls-Vargas
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For our experimental evaluation I have developed Voz, a narrative information extraction system that combines off-the-shelf natural language processing toolkits (e.g., Stanford Core NLP, Clear NLP), common sense knowledge (e.g., Word Net, Concept Net) and domain knowledge (Propp s narrative theory). We applied this methodology to an empirical study of our narrative information extraction pipeline (under review). |
| Researcher Affiliation | Academia | Josep Valls-Vargas Drexel University Philadelphia, Pennsylvania, USA EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions developing a system named Voz and combining off-the-shelf toolkits, but does not provide any specific link or statement about releasing the source code for their developed methodology. |
| Open Datasets | Yes | For this work we used an annotated dataset to compute a matrix from a story and compare it against a reference matrix using the Wordnet hierarchy to ο¬nd similarities. [Valls-Vargas et al., 2013]. We have been using a corpus of Slavic folktales collected and annotated by Mark A. Finlayson [2012]. |
| Dataset Splits | No | The paper mentions using an annotated dataset and a corpus for experimental evaluation, but does not specify any training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU models, CPU types, memory). It focuses on the conceptual and methodological aspects of the research. |
| Software Dependencies | No | The paper mentions using "off-the-shelf natural language processing toolkits (e.g., Stanford Core NLP, Clear NLP), common sense knowledge (e.g., Word Net, Concept Net)" but does not specify version numbers for these software dependencies. |
| Experiment Setup | No | The paper focuses on the general approach and contributions rather than detailed experimental setup. It does not provide specific hyperparameters, training configurations, or other system-level settings. |