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..
Exploring Multiple Feature Spaces for Novel Entity Discovery
Authors: Zhaohui Wu, Yang Song, C. Giles
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three real-world datasets show that our method significantly outperforms existing methods on identifying novel entities. |
| Researcher Affiliation | Collaboration | Zhaohui Wu , Yang Song , C. Lee Giles Computer Science and Engineering, Information Sciences and Technology Pennsylvania State University, University Park, PA 16802, USA Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methods in prose but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or a link to the source code for the methodology described. |
| Open Datasets | Yes | We use three datasets for our evaluation: 1) AIDA-EE dataset from Hoffart et al. (2014), 2) Web News dataset from a Web news portal, and 3) Wikievents dataset from Wikipedia event news.7 All three datasets use Wikipedia as the knowledge base for evaluation. ... 7http://www.cse.psu.edu/ zzw109/data.html |
| Dataset Splits | No | The paper describes training and test splits for the datasets (e.g., "training dataset and ... test dataset" for AIDA-EE, "used for training while ... used for testing" for Web News and Wiki Events), but does not explicitly mention a separate validation dataset split with specific details for their experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory specifications) used for running its experiments. |
| Software Dependencies | Yes | We use Stanford Dependencies4 that represent the grammatical relations between words using triplets: name of relation, governor and dependent, generated by the Stanford Parser 3.4 (Socher et al. 2013). |
| Experiment Setup | Yes | setting Num Trees=100, Num Leaves=20, Min Instances In Leaf=10, and Learning Rate=0.2. |