A Declarative Approach to Data-Driven Fact Checking
Authors: Julien Leblay147
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We describe the syntax and semantics of the language. We present algorithms to demonstrate its feasibility, and we illustrate its usefulness through examples. |
| Researcher Affiliation | Academia | Julien Leblay Artificial Intelligence Research Center, AIST, Japan |
| Pseudocode | Yes | Algorithm 1: Semi-Naıve Scope Query Evaluation |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper uses illustrative examples (e.g., Example 5, Example 6) to demonstrate the language and algorithms, but does not describe using any real dataset for training or evaluation, nor does it provide any information on public availability for such a dataset. |
| Dataset Splits | No | The paper does not describe any experimental setup involving datasets or their splits (training, validation, test) for reproducibility. |
| Hardware Specification | No | The paper does not provide any details about the specific hardware (e.g., GPU models, CPU types, memory) used for any computations or experiments. |
| Software Dependencies | No | The paper mentions theoretical frameworks like 'Datalog' and 'Markov Logic Networks' but does not specify any software dependencies with version numbers that would be needed to replicate the work. |
| Experiment Setup | No | The paper focuses on the theoretical framework, language, and algorithms. It does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings, as it does not report on empirical experiments. |