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 [1].
Efficient Online Learning for Mapping Kernels on Linguistic Structures
Authors: Giovanni Da San Martino, Alessandro Sperduti, Fabio Aiolli, Alessandro Moschitti3421-3428
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, we derive a reliable empirical evidence on semantic role labeling task, which is a natural language classification task, highly dependent on syntactic trees. The results show that our faster approach can clearly improve on standard kernel-based SVMs, which cannot run on very large datasets. |
| Researcher Affiliation | Collaboration | Giovanni Da San Martino Qatar Computing Research Institute, Hamad Bin Khalifa University Doha, Qatar EMAIL Alessandro Sperduti, Fabio Aiolli Department of Mathematics, University of Padova via Trieste, 63, Padova, Italy EMAIL Alessandro Moschitti Amazon Manhattan Beach, CA, USA EMAIL |
| Pseudocode | No | The paper describes algorithmic procedures and equations, such as the kernelized perceptron and the score computation, but does not present them within clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | As a referring dataset, we used Prop Bank along with Penn Tree bank 2 (Marcus, Santorini, and Marcinkiewicz 1993). |
| Dataset Splits | Yes | Table 1: Statistics of syntactic trees in the boundary detection dataset. Training Validation Test Num. of trees 4,079,510 234,416 149,140 |
| Hardware Specification | No | The paper discusses computational time and memory usage but does not provide specific details about the hardware (e.g., CPU, GPU models, or memory specifications) used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation of the experiments. |
| Experiment Setup | Yes | In the case of the TKs and combination, we used the following parameters: λ {0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0} (TKs decay factor) and γ {0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9} (weighting the linear combination between tree and polynomial kernels). |