Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition
Authors: Yangming Li, lemao liu, Shuming Shi
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through empirical studies performed on synthetic datasets, we find two causes of performance degradation. |
| Researcher Affiliation | Industry | Yangming Li, Lemao Liu, & Shuming Shi Tencent AI Lab {newmanli,redmondliu,shumingshi}@tencent.com |
| Pseudocode | No | The paper describes methods in prose and with diagrams (Figure 3) but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Our source code is available at https://github.com/LeePleased/NegSampling-NER. |
| Open Datasets | Yes | The material datasets are Co NLL-2003 (Sang & De Meulder, 2003) and Onto Notes 5.0 (Pradhan et al., 2013). |
| Dataset Splits | Yes | The data contains 2400 sentences tagged by human annotators and are divided into three parts: 1200 for training, 400 for dev, and 800 for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Adam for optimization but does not provide specific version numbers for software dependencies or libraries (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | L2 regularization and dropout ratio are respectively set as 1 10 5 and 0.4 for reducing overfit. The dimension of scoring layers is 256. Ratio λ is set as 0.35. When the sentence encoder is LSTM, we set the hidden dimension as 512 and use pretrained word embeddings (Pennington et al., 2014; Song et al., 2018) to initialize word representations. We utilize Adam (Kingma & Ba, 2014) as the optimization algorithm and adopt the suggested hyper-parameters. |