Learning of Structurally Unambiguous Probabilistic Grammars
Authors: Dolav Nitay, Dana Fisman, Michal Ziv-Ukelson9170-9178
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As a proof-of-concept, in Section 6 we exemplify our algorithm by applying it to a small data-set of genomic data. |
| Researcher Affiliation | Academia | Dolav Nitay*, Dana Fisman, Michal Ziv-Ukelson Ben Gurion University dolavn@post.bgu.ac.il, dana@cs.bgu.ac.il, michaluz@bgu.ac.il |
| Pseudocode | Yes | Algorithm 1 Learn CMTA(T, C, H, B). ... Algorithm 5 Extract CMTA |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper mentions using 'a small data-set of genomic data' and an 'MDR dataset' for demonstration, but it does not provide concrete access information (e.g., a link, DOI, or specific citation with author and year for the dataset) to make it publicly available. |
| Dataset Splits | No | The paper mentions applying the algorithm to 'genomic data' and 'gene-cluster grammars' as a demonstration, but it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running its experiments or demonstrations. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, specific libraries or solvers) used for its implementation or experiments. |
| Experiment Setup | No | The paper describes a demonstration on genomic data, showing a learned PCFG. However, it does not provide specific details about the experimental setup, such as hyperparameters, optimizer settings, training configurations, or other system-level settings for the learning algorithm. |