Effective Broad-Coverage Deep Parsing
Authors: James Allen, Omid Bahkshandeh, William de Beaumont, Lucian Galescu, Choh Man Teng
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate these customizations with some ablation experiments. |
| Researcher Affiliation | Academia | IHMC, 40 S. Alcaniz St, Pensacola, FL 32501 {jallen, omidb, wbeaumont, lgalescu, cmteng}@ihmc.us |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper provides links to the general TRIPS parser system and its lexicon/ontology (e.g., http://trips.ihmc.us/parser and www.cs.rochester.edu/research/trips/lexicon/browse-ont-lex.html), but does not explicitly state that the source code for the specific methodology or customizations described in this paper is available. |
| Open Datasets | No | While the experimental data is extracted from Pub Med Central, the specific set of 60 sentences and their gold annotations are not made publicly available with a link, DOI, or formal citation for the dataset as used in the experiment. |
| Dataset Splits | No | The paper mentions using 60 sentences and constructing a 'test set', but does not specify any train/validation/test splits, percentages, or absolute counts for dataset partitioning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions several software tools used (e.g., 'Stanford part-of-speech tagger', 'Enju parser'), but does not provide specific version numbers for these software components, which is necessary for reproducibility. |
| Experiment Setup | Yes | All the ablation tests in Figure 4 were performed with a bracket crossing penalty of 0.99 and a boosting parameter of 0.2. |