Exploring Multiple Feature Spaces for Novel Entity Discovery
Authors: Zhaohui Wu, Yang Song, C. Giles
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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 zzw109@psu.edu, yangsong@microsoft.com, giles@ist.psu.edu |
| 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. |