A Learning Error Analysis for Structured Prediction with Approximate Inference
Authors: Yuanbin Wu, Man Lan, Shiliang Sun, Qi Zhang, Xuanjing Huang
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations on text classification, sequential labelling and dependency parsing witness the success of approximate inference and the benefit of the proposed margin. |
| Researcher Affiliation | Academia | 1School of Computer Science and Software Engineering, East China Normal University 2Shanghai Key Laboratory of Multidimensional Information Processing 3School of Computer Science, Fudan University |
| Pseudocode | Yes | Algorithm 3: Structured perceptron with mρ. and Algorithm 4: Online PA with mρ. |
| Open Source Code | No | The paper references third-party tools like SVMmulticlass and MSTParser, but does not provide a link or statement about the authors' own open-source code for the described methodology. |
| Open Datasets | Yes | We report results on the 20 newsgroups corpus (18000 documents, 20 classes). We use the Co NLL 2000 dataset [Sang and Buchholz, 2000] for chunking and POS tagging, SIGHAN 2005 bake-off corpus (pku and msr) [Emerson, 2005] for word segmentation. |
| Dataset Splits | Yes | We take 20% of the training set as development set for tuning ρ (grid search in [0, 2] with step size 0.05). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for the experiments. |
| Software Dependencies | Yes | The implementation is adapted from SVMmulticlass 3. |
| Experiment Setup | Yes | We take 20% of the training set as development set for tuning ρ (grid search in [0, 2] with step size 0.05). We use Algorithm 3 with 20 iterations and learning step 1. |