Stacking With Auxiliary Features
Authors: Nazneen Fatema Rajani, Raymond J. Mooney
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experimental Results |
| Researcher Affiliation | Academia | Nazneen Fatema Rajani Department of Computer Science University of Texas at Austin nrajani@cs.utexas.edu Raymond J. Mooney Department of Computer Science University of Texas at Austin mooney@cs.utexas.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We use SWAF to demonstrate new state-of-the-art results on two of the three tasks... The first two are in NLP and part of the NIST Knowledge Base Population (KBP) challenge Cold Start Slot-Filling (CSSF) and Entity Discovery and Linking (EDL) [Ji et al., 2016]. The third task is in computer vision and part of the Image Net 2015 challenge [Russakovsky et al., 2015] Object Detection from images. |
| Dataset Splits | Yes | A small random sample of the training set (10%) was used as validation data to set the hyper-parameters for each of the tasks. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments are provided. |
| Software Dependencies | No | The paper mentions software like 'Caffe system' and 'word2vec model', but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For the two NLP tasks, we used an L1 regularized linear SVM weighted by the number of instances for each class, as a meta-classifier for stacking. For the object detection task, we used an SVM with an RBF kernel. ... we first embed words into vectors by training a word2vec model [Mikolov et al., 2013] with word vector dimension of 100, window-size set to 8, 10 negative samples and 10 iterations on Free Base. |