Solving Semantic Problems Using Contexts Extracted from Knowledge Graphs
Authors: Adrian Boteanu
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented our topic modeling approach in two projects. The first uses an interactive story tablet application, Tinkr Book... In the second application, we use topics to predict ratings over the Yelp Academic Dataset. We are evaluating the analogy solving system on SAT questions. |
| Researcher Affiliation | Academia | Adrian Boteanu aboteanu@wpi.edu Worcester Polytechnic Institute |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | For our work, we choose a freely available knowledge base which meets this criteria, Concept Net (Havasi, Speer, and Alonso 2007)... In the second application, we use topics to predict ratings over the Yelp Academic Dataset. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, and test dataset splits, such as percentages, sample counts, or explicit splitting methodologies. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, or memory) used for experiments are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |