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..
Quantum-Inspired Representation for Long-Tail Senses of Word Sense Disambiguation
Authors: Junwei Zhang, Ruifang He, Fengyu Guo
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We theoretically prove the correctness of the method, and verify its effectiveness under the standard WSD evaluation framework and obtain state-of-the-art performance. Furthermore, we also test on the constructed LTS and the latest cross-lingual datasets, and achieve promising results. |
| Researcher Affiliation | Academia | 1 Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, China. 2 College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | Other hyperparameters not listed will be given in the published code. |
| Open Datasets | Yes | To evaluate the effectiveness of QR-WSD, we carried out experiments under two evaluation settings, namely the standardized evaluation setting and the enhanced evaluation setting. The standardized setting includes only Sem Cor2 in the training set; the enhanced setting includes Sem Cor and WNGT3 in the training set. (2http://lcl.uniroma1.it/wsdeval/training-data, 3https://wordnetcode.princeton.edu/glosstag.shtml) |
| Dataset Splits | Yes | Sem Eval07 (SE7; Pradhan et al. (2007)), following convention (Kumar et al. 2019; Blevins and Zettlemoyer 2020), is regarded as the development set. |
| Hardware Specification | Yes | The computing platform of the program is Ubuntu 18.04, which is equipped with two Tesla P40 GPUs. |
| Software Dependencies | Yes | The program is developed based on the Pytorch 1.8 framework and written in Python 3.6. Moreover, Word Net 3.0 is provided by NLTK 3.5, and bert-base-uncased and bert-large-uncased are provided by Transformers 4.5. |
| Experiment Setup | Yes | The learning rate, epoch and batch size of the model are {1e-5, 5e-6}, 20 and 4 respectively. Other hyperparameters not listed will be given in the published code. |