A Sequence Labeling Approach to Deriving Word Variants
Authors: Jennifer D'Souza
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present our work in the following sections beginning with the experimental data creation process, followed by details on our adopted approach, and concluding with an evaluation of the approach.Our results show that this learning-based approach is feasible for the task and warrants further exploration. |
| Researcher Affiliation | Academia | Jennifer D Souza Human Language Technology Research Institute University of Texas at Dallas, Richardson, TX 75083-0688 jennifer.l.dsouza@utdallas.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper refers to third-party tools (CRF++, DirecTL+) being available, but does not provide concrete access to the authors' own source code for the methodology described in the paper. |
| Open Datasets | No | The paper describes the creation of a dataset from clinical notes, filtered with terms from SNOMED clinical terminology, Word Net (Miller 1995), or the online Webster's dictionary. While Word Net is cited, the custom-created final dataset (12,057 unique words, 4609 word groups) does not have concrete access information (link, DOI, repository) provided for public availability. |
| Dataset Splits | No | The paper mentions using a 'subset of the dataset' for training (3885 unique words, 1500 word groups) and 'the remaining dataset' for testing, but does not provide specific details on train/validation/test splits, percentages, or explicit use of a validation set for reproduction. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running the experiments are provided. |
| Software Dependencies | No | The paper mentions using 'DirecTL+' and 'CRF++', but does not provide specific version numbers for these software dependencies, which are necessary for reproducible descriptions. |
| Experiment Setup | No | The paper describes the features used and how training and test instances are created, but it does not provide specific experimental setup details such as concrete hyperparameter values or detailed training configurations (e.g., learning rate, batch size, epochs for the CRF++ model). |