A Generalized Idiom Usage Recognition Model Based on Semantic Compatibility
Authors: Changsheng Liu, Rebecca Hwa6738-6745
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on two benchmark idiom corpora; results suggest that the proposed generalized model achieves competitive results compared to state-of-the-art per-idiom models. |
| Researcher Affiliation | Academia | Changsheng Liu, Rebecca Hwa Computer Science Department University of Pittsburgh Pittsburgh, PA 15260, USA {changsheng, hwa}@cs.pitt.edu |
| Pseudocode | No | The paper describes its model using figures and mathematical equations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links or explicit statements about releasing source code for the described methodology. |
| Open Datasets | Yes | We conduct experiments on two benchmark idiom corpora: Sem Eval 2013 Task 5B corpus (Korkontzelos et al. 2013) and Verb Noun Combination (VNC) dataset (Cook, Fazly, and Stevenson 2008). |
| Dataset Splits | No | The paper mentions picking '10 idioms that are different from the evaluation set' to tune a bias term ('bu'), implying a development set, but it does not provide specific details on standard training/validation/test splits (e.g., percentages or sample counts) for the main experimental setup. It mentions '5-fold cross validation' for baselines, but not for its own model's splits. |
| Hardware Specification | No | The paper describes the training process and hyperparameters but does not provide specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' and 'word2vec' but does not specify version numbers for any software dependencies, libraries, or programming languages. |
| Experiment Setup | Yes | The hyperparameters are summarized in Table 1. Parameter Value word embedding size 200 context embedding size 200 LSTM hidden size 200 f1 input/output size 200/400 f2 input/output size 400/200 negative samples 15 epoch 10 batch size 500 learning rate 0.001 |