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..
Semantic Representation
Authors: Lenhart Schubert
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper provides a brief opinionated survey of broad-coverage semantic representation (SR). It suggests multiple desiderata for such representations, and then outlines more than a dozen approaches to SR some longstanding, and some more recent, providing quick characterizations, pros, cons, and some comments on implementations. |
| Researcher Affiliation | Academia | Lenhart Schubert University of Rochester Rochester, NY 14627-0226 |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for any methodology described within the paper, as it is a survey of existing methods. |
| Open Datasets | No | The paper is a survey and does not report on experiments that would involve training data. Therefore, no access information for a public dataset is provided. |
| Dataset Splits | No | The paper is a survey and does not report on experiments that would involve dataset splits. Therefore, no specific dataset split information is provided. |
| Hardware Specification | No | The paper is a survey and does not report on experiments, thus no specific hardware details used for running experiments are mentioned. |
| Software Dependencies | No | The paper is a survey and does not report on experiments, thus no specific ancillary software details with version numbers are provided. |
| Experiment Setup | No | The paper is a survey and does not report on experiments, thus no specific experimental setup details like hyperparameters or training configurations are provided. |