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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Saul: Towards Declarative Learning Based Programming
Authors: Parisa Kordjamshidi, Dan Roth, Hao Wu
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Table 1 shows the experimental results of running the learning and inference models described in Section 4.1 on the Co NLL-04 data for the Entity-mention-Relation (see Section 3). |
| Researcher Affiliation | Academia | Parisa Kordjamshidi, Dan Roth, Hao Wu University of Illinois at Urbana-Champaign EMAIL |
| Pseudocode | No | The paper includes Scala code snippets, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | Saul (footnote 3): http://cogcomp.cs.illinois.edu/page/software view/Saul |
| Open Datasets | Yes | Table 1 shows the experimental results... on the Co NLL-04 data for the Entity-mention-Relation (see Section 3). |
| Dataset Splits | Yes | 5-fold cross validation, Co NLL-04 dataset |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions that Saul is "written in Scala", but does not provide specific version numbers for Scala or any other software dependencies, libraries, or solvers. |
| Experiment Setup | No | The paper states in footnote 2: "Setting specific algorithms parameters can be done by the programmer, or automatically by Saul; these details are omitted." This indicates that specific experimental setup details, such as hyperparameters or training parameters, are not provided. |