Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition
Authors: Yangming Li, lemao liu, Shuming Shi
ICLR 2021 | Venue PDF | 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 EMAIL |
| 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. |