Efficient Online Learning for Mapping Kernels on Linguistic Structures
Authors: Giovanni Da San Martino, Alessandro Sperduti, Fabio Aiolli, Alessandro Moschitti3421-3428
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 gmartino@hbku.edu.qa Alessandro Sperduti, Fabio Aiolli Department of Mathematics, University of Padova via Trieste, 63, Padova, Italy {sperduti, aiolli}@math.unipd.it Alessandro Moschitti Amazon Manhattan Beach, CA, USA amosch@amazon.com |
| 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). |