Saul: Towards Declarative Learning Based Programming
Authors: Parisa Kordjamshidi, Dan Roth, Hao Wu
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 {kordjam,danr,haowu4}@illinois.edu |
| 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 speciļ¬c 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. |