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Yuefei Wu

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3 papers

1 author row

AAAI Conference 2024 Conference Paper

The Evidence Contraction Issue in Deep Evidential Regression: Discussion and Solution

Yuefei Wu
Bin Shi
Bo Dong
Qinghua Zheng
Hua Wei

Deep Evidential Regression (DER) places a prior on the original Gaussian likelihood and treats learning as an evidence acquisition process to quantify uncertainty. For the validity of the evidence theory, DER requires specialized activation functions to ensure that the prior parameters remain non-negative. However, such constraints will trigger evidence contraction, causing sub-optimal performance. In this paper, we analyse DER theoretically, revealing the intrinsic limitations for sub-optimal performance: the non-negativity constraints on the Normal Inverse-Gamma (NIG) prior parameter trigger the evidence contraction under the specialized activation function, which hinders the optimization of DER performance. On this basis, we design a Non-saturating Uncertainty Regularization term, which effectively ensures that the performance is further optimized in the right direction. Experiments on real-world datasets show that our proposed approach improves the performance of DER while maintaining the ability to quantify uncertainty.

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IJCAI Conference 2023 Conference Paper

NerCo: A Contrastive Learning Based Two-Stage Chinese NER Method

Zai Zhang
Bin Shi
Haokun Zhang
Huang Xu
Yaodong Zhang
Yuefei Wu
Bo Dong
Qinghua Zheng

Sequence labeling serves as the most commonly used scheme for Chinese named entity recognition(NER). However, traditional sequence labeling methods classify tokens within an entity into different classes according to their positions. As a result, different tokens in the same entity may be learned with representations that are isolated and unrelated in target representation space, which could finally negatively affect the subsequent performance of token classification. In this paper, we point out and define this problem as Entity Representation Segmentation in Label-semantics. And then we present NerCo: Named entity recognition with Contrastive learning, a novel NER framework which can better exploit labeled data and avoid the above problem. Following the pretrain-finetune paradigm, NerCo firstly guides the encoder to learn powerful label-semantics based representations by gathering the encoded token representations of the same Semantic Class while pushing apart that of different. Subsequently, NerCo finetunes the learned encoder for final entity prediction. Extensive experiments on several datasets demonstrate that our framework can consistently improve the baseline and achieve state-of-the-art performance.

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IS Journal 2020 Journal Article

Recognizing Nested Named Entity Based on the Neural Network Boundary Assembling Model

Yanping Chen
Yuefei Wu
Yongbin Qin
Ying Hu
Zeyu Wang
Ruizhang Huang
Xinyu Cheng
Ping Chen

The task to recognize named entities is often modeled as a sequence labeling process, which selects a label path whose probability is maximum for an input sentence. Because it makes the assumption that the input sentence has a flattened structure, it often fails to recognize nested named entities. In our previous work, a boundary assembling (BA) model was proposed. It is a cascading framework, which identifies named entity boundaries first, and then assembles them into entity candidates for further assessment. This model is effective to recognize nested named entities, but still suffers from poor performance caused by the sparse feature problem. In this article, the BA model is remodeled with the advancement of neural networks, which enables the model to capture semantic information of a sentence by using word embeddings pretrained in external resources. In our experiments, it shows an impressive improvement on the final performance, outperforming the state of the art more than 17% in F-score.

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