Hierarchical Contextualized Representation for Named Entity Recognition
Authors: Ying Luo, Fengshun Xiao, Hai Zhao8441-8448
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on three benchmark NER datasets (Co NLL-2003 and Ontonotes 5.0 English datasets, Co NLL-2002 Spanish dataset) show that we establish new state-of-the-art results. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering, Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, Shanghai, China Mo E Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China {kingln, felixxiao}@sjtu.edu.cn, zhaohai@cs.sjtu.edu.cn |
| Pseudocode | No | The paper describes the model architecture and components with equations and figures, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code will be available at https://github.com/cslydia/HireNER. |
| Open Datasets | Yes | Our proposed representations are evaluated on three benchmark NER datasets: Co NLL-2003 (Sang and De Meulder 2003) and Onto Notes 5.0 (Pradhan et al. 2013) English NER datasets, Co NLL-2002 Spanish NER (Tjong Kim Sang 2002) dataset. |
| Dataset Splits | Yes | Co NLL-2003 English NER consists of 22,137 sentences totally and is split into 14,987, 3,466 and 3,684 sentences for the training, development set and test sets, respectively. ... Co NLL-2002 Spanish NER consists of 11,752 sentences totally and is split into 8,322, 1,914 and 1,516 sentences for the training, development and test sets, respectively. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or cloud computing instance specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions software like GloVe embeddings and Int Net, but it does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The batch size is set as 10, the initial learning rate is set to 0.015 and will shrunk by 5% after each epoch. The hidden size of sequence labeling encoder and the sentence-level encoder are set as 256 and 128, respectively. We apply dropout to embeddings and hidden states with a rate of 0.5. The λ used to fuse original hidden state and document-level representation is set as 0.3 empirically. |