An Online Learning Algorithm for Bilinear Models
Authors: Yuanbin Wu, Shiliang Sun
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two sequential labelling tasks give positive results. We conduct experiments on two sequential labelling tasks: word segmentation and text chunking. |
| Researcher Affiliation | Academia | Yuanbin Wu YBWU@CS.ECNU.EDU.CN Shiliang Sun SLSUN@CS.ECNU.EDU.CN Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology, East China Normal University |
| Pseudocode | Yes | Algorithm 1 Blockwise Coordinate Descent. Algorithm 2 Online Learning of the Bilinear Model. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The data set is from the second SIGHAN Backoff (Emerson, 2005). We conduct experiments on the Co NLL Sharedtask 2000 (Sang & Buchholz, 2000). |
| Dataset Splits | Yes | The value R is selected on a dev set (10% of the pku training set). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'SVM solver' and 'CRF' (crf2, crf1 from crfpp.googlecode.com/) but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The algorithms for comparison are: bol which is the proposed method with T = 20, C = 1, R = 4, bcd which is the blockwise coordinate descent with SVM solver in Shalev-Shwartz & Singer (2006) (C = 1). We also compare state-of-the-art online linear model sp which is the structured perceptron with T = 20, learning rate C = 1. |