Business Event Curation: Merging Human and Automated Approaches
Authors: Yiqi Wang, Huiying Ma, Nichola Lowe, Maryann Feldman, Charles Schmitt
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We used 25 news articles, 9689 words in total, as preliminary test data for our improved NER approach. As Table1.1 suggests, our approach we significantly improved the Stanford NER system performance on our data set. |
| Researcher Affiliation | Academia | Yiqi Wang1, Huiying Ma1, Nichola Lowe1, Maryann Feldman1, Charles Schmitt1 1 University of North Carolina at Chapel Hill (UNC), Chapel Hill, NC 27599, USA. {wangyiqi, huiying, nlowe}@email.unc.edu; maryann.feldman@unc.edu; cschmitt@renci.org |
| Pseudocode | No | The paper describes steps for its methods but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper states 'We used 25 news articles, 9689 words in total, as preliminary test data for our improved NER approach.' but does not provide concrete access information (link, DOI, specific citation) for these articles or the full dataset. |
| Dataset Splits | No | The paper mentions 'preliminary test data' but does not provide specific details on training, validation, or test splits, such as percentages, absolute counts, or methods for creating splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Stanford NER system', 'Stanford CR system', and 'Apache Solr', but does not specify their version numbers. |
| Experiment Setup | No | The paper describes the methods conceptually but does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size) or training configurations. |