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Journal articles on the topic 'Multi-label embedding'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Gupta, Vivek, Rahul Wadbude, Nagarajan Natarajan, Harish Karnick, Prateek Jain, and Piyush Rai. "Distributional Semantics Meets Multi-Label Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3747–54. http://dx.doi.org/10.1609/aaai.v33i01.33013747.

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We present a label embedding based approach to large-scale multi-label learning, drawing inspiration from ideas rooted in distributional semantics, specifically the Skip Gram Negative Sampling (SGNS) approach, widely used to learn word embeddings. Besides leading to a highly scalable model for multi-label learning, our approach highlights interesting connections between label embedding methods commonly used for multi-label learning and paragraph embedding methods commonly used for learning representations of text data. The framework easily extends to incorporating auxiliary information such as

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Zhu, Pengfei, Qi Hu, Qinghua Hu, Changqing Zhang, and Zhizhao Feng. "Multi-view label embedding." Pattern Recognition 84 (December 2018): 126–35. http://dx.doi.org/10.1016/j.patcog.2018.07.009.

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You, Renchun, Zhiyao Guo, Lei Cui, Xiang Long, Yingze Bao, and Shilei Wen. "Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12709–16. http://dx.doi.org/10.1609/aaai.v34i07.6964.

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Multi-label image and video classification are fundamental yet challenging tasks in computer vision. The main challenges lie in capturing spatial or temporal dependencies between labels and discovering the locations of discriminative features for each class. In order to overcome these challenges, we propose to use cross-modality attention with semantic graph embedding for multi-label classification. Based on the constructed label graph, we propose an adjacency-based similarity graph embedding method to learn semantic label embeddings, which explicitly exploit label relationships. Then our nove

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Zhang, Jujie, Min Fang, and Huimin Chai. "Multi-label local discriminative embedding." Journal of Systems Engineering and Electronics 28, no. 5 (2017): 1009–18. http://dx.doi.org/10.21629/jsee.2017.05.19.

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Shi, Min, Yufei Tang, and Xingquan Zhu. "MLNE: Multi-Label Network Embedding." IEEE Transactions on Neural Networks and Learning Systems 31, no. 9 (2020): 3682–95. http://dx.doi.org/10.1109/tnnls.2019.2945869.

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Shi, Yaxin, Donna Xu, Yuangang Pan, Ivor W. Tsang, and Shirui Pan. "Label Embedding with Partial Heterogeneous Contexts." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4926–33. http://dx.doi.org/10.1609/aaai.v33i01.33014926.

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Label embedding plays an important role in many real-world applications. To enhance the label relatedness captured by the embeddings, multiple contexts can be adopted. However, these contexts are heterogeneous and often partially observed in practical tasks, imposing significant challenges to capture the overall relatedness among labels. In this paper, we propose a general Partial Heterogeneous Context Label Embedding (PHCLE) framework to address these challenges. Categorizing heterogeneous contexts into two groups, relational context and descriptive context, we design tailor-made matrix facto

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Kumar, Vikas, Arun K. Pujari, Vineet Padmanabhan, and Venkateswara Rao Kagita. "Group preserving label embedding for multi-label classification." Pattern Recognition 90 (June 2019): 23–34. http://dx.doi.org/10.1016/j.patcog.2019.01.009.

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Huang, Kuan-Hao, and Hsuan-Tien Lin. "Cost-sensitive label embedding for multi-label classification." Machine Learning 106, no. 9-10 (2017): 1725–46. http://dx.doi.org/10.1007/s10994-017-5659-z.

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Huang, Jun, Qian Xu, Xiwen Qu, Yaojin Lin, and Xiao Zheng. "Improving Multi-Label Learning by Correlation Embedding." Applied Sciences 11, no. 24 (2021): 12145. http://dx.doi.org/10.3390/app112412145.

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In multi-label learning, each object is represented by a single instance and is associated with more than one class labels, where the labels might be correlated with each other. As we all know, exploiting label correlations can definitely improve the performance of a multi-label classification model. Existing methods mainly model label correlations in an indirect way, i.e., adding extra constraints on the coefficients or outputs of a model based on a pre-learned label correlation graph. Meanwhile, the high dimension of the feature space also poses great challenges to multi-label learning, such

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Kumar, Vikas, Arun K. Pujari, Vineet Padmanabhan, Sandeep Kumar Sahu, and Venkateswara Rao Kagita. "Multi-label classification using hierarchical embedding." Expert Systems with Applications 91 (January 2018): 263–69. http://dx.doi.org/10.1016/j.eswa.2017.09.020.

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Li, Yachong, and Youlong Yang. "Label Embedding for Multi-label Classification Via Dependence Maximization." Neural Processing Letters 52, no. 2 (2020): 1651–74. http://dx.doi.org/10.1007/s11063-020-10331-7.

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Wang, Ya, Dongliang He, Fu Li, et al. "Multi-Label Classification with Label Graph Superimposing." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12265–72. http://dx.doi.org/10.1609/aaai.v34i07.6909.

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Images or videos always contain multiple objects or actions. Multi-label recognition has been witnessed to achieve pretty performance attribute to the rapid development of deep learning technologies. Recently, graph convolution network (GCN) is leveraged to boost the performance of multi-label recognition. However, what is the best way for label correlation modeling and how feature learning can be improved with label system awareness are still unclear. In this paper, we propose a label graph superimposing framework to improve the conventional GCN+CNN framework developed for multi-label recogni

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Gong, Xiuwen, Dong Yuan, and Wei Bao. "Partial Multi-Label Learning via Large Margin Nearest Neighbour Embeddings." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6729–36. http://dx.doi.org/10.1609/aaai.v36i6.20628.

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To deal with ambiguities in partial multi-label learning (PML), existing popular PML research attempts to perform disambiguation by direct ground-truth label identification. However, these approaches can be easily misled by noisy false-positive labels in the iteration of updating the model parameter and the latent ground-truth label variables. When labeling information is ambiguous, we should depend more on underlying structure of data, such as label and feature correlations, to perform disambiguation for partially labeled data. Moreover, large margin nearest neighbour (LMNN) is a popular stra

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Lin, Yaojin, Qinghua Hu, Jinghua Liu, Xingquan Zhu, and Xindong Wu. "MULFE: Multi-Label Learning via Label-Specific Feature Space Ensemble." ACM Transactions on Knowledge Discovery from Data 16, no. 1 (2021): 1–24. http://dx.doi.org/10.1145/3451392.

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In multi-label learning, label correlations commonly exist in the data. Such correlation not only provides useful information, but also imposes significant challenges for multi-label learning. Recently, label-specific feature embedding has been proposed to explore label-specific features from the training data, and uses feature highly customized to the multi-label set for learning. While such feature embedding methods have demonstrated good performance, the creation of the feature embedding space is only based on a single label, without considering label correlations in the data. In this artic

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Liu, Chengliang, Jinlong Jia, Jie Wen, et al. "Attention-Induced Embedding Imputation for Incomplete Multi-View Partial Multi-Label Classification." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13864–72. http://dx.doi.org/10.1609/aaai.v38i12.29293.

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As a combination of emerging multi-view learning methods and traditional multi-label classification tasks, multi-view multi-label classification has shown broad application prospects. The diverse semantic information contained in heterogeneous data effectively enables the further development of multi-label classification. However, the widespread incompleteness problem on multi-view features and labels greatly hinders the practical application of multi-view multi-label classification. Therefore, in this paper, we propose an attention-induced missing instances imputation technique to enhance the

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He, Sunan, Taian Guo, Tao Dai, et al. "Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 808–16. http://dx.doi.org/10.1609/aaai.v37i1.25159.

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Real-world recognition system often encounters the challenge of unseen labels. To identify such unseen labels, multi-label zero-shot learning (ML-ZSL) focuses on transferring knowledge by a pre-trained textual label embedding (e.g., GloVe). However, such methods only exploit single-modal knowledge from a language model, while ignoring the rich semantic information inherent in image-text pairs. Instead, recently developed open-vocabulary (OV) based methods succeed in exploiting such information of image-text pairs in object detection, and achieve impressive performance. Inspired by the success

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Pu, Juhua, Zhuang Liu, Yujun Chen, and Xingwu Liu. "MEMN:Multiple Vectors Embedding for Multi-Label Networks." IEEE Access 6 (2018): 66143–52. http://dx.doi.org/10.1109/access.2018.2878870.

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Park, Sunho, and Seungjin Choi. "Max-margin embedding for multi-label learning." Pattern Recognition Letters 34, no. 3 (2013): 292–98. http://dx.doi.org/10.1016/j.patrec.2012.10.016.

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Liu, Siyu, Xuehua Song, Zhongchen Ma, Ernest Domanaanmwi Ganaa, and XiangJun Shen. "MoRE: Multi-output residual embedding for multi-label classification." Pattern Recognition 126 (June 2022): 108584. http://dx.doi.org/10.1016/j.patcog.2022.108584.

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20

Wang, Hua, Heng Huang, and Chris Ding. "Discriminant Laplacian Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 618–23. http://dx.doi.org/10.1609/aaai.v24i1.7662.

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Many real life applications brought by modern technologies often have multiple data sources, which are usually characterized by both attributes and pairwise similarities at the same time. For example in webpage ranking, a webpage is usually represented by a vector of term values, and meanwhile the internet linkages induce pairwise similarities among the webpages. Although both attributes and pairwise similarities are useful for class membership inference, many traditional embedding algorithms only deal with one type of input data. In order to make use of the both types of data simultaneously,

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Chen, Chen, Haobo Wang, Weiwei Liu, Xingyuan Zhao, Tianlei Hu, and Gang Chen. "Two-Stage Label Embedding via Neural Factorization Machine for Multi-Label Classification." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3304–11. http://dx.doi.org/10.1609/aaai.v33i01.33013304.

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Label embedding has been widely used as a method to exploit label dependency with dimension reduction in multilabel classification tasks. However, existing embedding methods intend to extract label correlations directly, and thus they might be easily trapped by complex label hierarchies. To tackle this issue, we propose a novel Two-Stage Label Embedding (TSLE) paradigm that involves Neural Factorization Machine (NFM) to jointly project features and labels into a latent space. In encoding phase, we introduce a Twin Encoding Network (TEN) that digs out pairwise feature and label interactions in

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Liu, Huiting, Geng Chen, Peipei Li, Peng Zhao, and Xindong Wu. "Multi-label text classification via joint learning from label embedding and label correlation." Neurocomputing 460 (October 2021): 385–98. http://dx.doi.org/10.1016/j.neucom.2021.07.031.

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23

Ma, Jianghong, Zhaoyang Tian, Haijun Zhang, and Tommy W. S. Chow. "Multi-Label Low-dimensional Embedding with Missing Labels." Knowledge-Based Systems 137 (December 2017): 65–82. http://dx.doi.org/10.1016/j.knosys.2017.09.005.

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Liu, Chengliang, Jie Wen, Xiaoling Luo, and Yong Xu. "Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8816–24. http://dx.doi.org/10.1609/aaai.v37i7.26060.

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As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multi-view multi-label learning widely applicable for various pattern recognition tasks. In this complex representation learning problem, three main challenges can be characterized as follows: i) How to learn consistent representations of samples across all views? ii) How to exploit and utilize category correlations of multi-label to guide inference? iii) How to avoid the negative impact resulting from the incompleteness of vie

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Wang, Fengjun, Sarai Mizrachi, Moran Beladev, et al. "MuMIC – Multimodal Embedding for Multi-Label Image Classification with Tempered Sigmoid." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15603–11. http://dx.doi.org/10.1609/aaai.v37i13.26850.

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Multi-label image classification is a foundational topic in various domains. Multimodal learning approaches have recently achieved outstanding results in image representation and single-label image classification. For instance, Contrastive Language-Image Pretraining (CLIP) demonstrates impressive image-text representation learning abilities and is robust to natural distribution shifts. This success inspires us to leverage multimodal learning for multi-label classification tasks, and benefit from contrastively learnt pretrained models. We propose the Multimodal Multi-label Image Classification

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26

Wang, Haobo, Chen Chen, Weiwei Liu, Ke Chen, Tianlei Hu, and Gang Chen. "Incorporating Label Embedding and Feature Augmentation for Multi-Dimensional Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6178–85. http://dx.doi.org/10.1609/aaai.v34i04.6083.

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Feature augmentation, which manipulates the feature space by integrating the label information, is one of the most popular strategies for solving Multi-Dimensional Classification (MDC) problems. However, the vanilla feature augmentation approaches fail to consider the intra-class exclusiveness, and may achieve degenerated performance. To fill this gap, a novel neural network based model is proposed which seamlessly integrates the Label Embedding and Feature Augmentation (LEFA) techniques to learn label correlations. Specifically, based on attentional factorization machine, a cross correlation

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Armandpour, Mohammadreza, Patrick Ding, Jianhua Huang, and Xia Hu. "Robust Negative Sampling for Network Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3191–98. http://dx.doi.org/10.1609/aaai.v33i01.33013191.

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Many recent network embedding algorithms use negative sampling (NS) to approximate a variant of the computationally expensive Skip-Gram neural network architecture (SGA) objective. In this paper, we provide theoretical arguments that reveal how NS can fail to properly estimate the SGA objective, and why it is not a suitable candidate for the network embedding problem as a distinct objective. We show NS can learn undesirable embeddings, as the result of the “Popular Neighbor Problem.” We use the theory to develop a new method “R-NS” that alleviates the problems of NS by using a more intelligent

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Zhang, Pingyue, and Mengyue Wu. "Multi-Label Supervised Contrastive Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16786–93. http://dx.doi.org/10.1609/aaai.v38i15.29619.

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Multi-label classification is an arduous problem given the complication in label correlation. Whilst sharing a common goal with contrastive learning in utilizing correlations for representation learning, how to better leverage label information remains challenging. Previous endeavors include extracting label-level presentations or mapping labels to an embedding space, overlooking the correlation between multiple labels. It exhibits a great ambiguity in determining positive samples with different extent of label overlap between samples and integrating such relations in loss functions. In our wo

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Chen, Pei, Zhuo Zhang, Yang Lei, Ke Niu, and Xiaoyuan Yang. "A Multi-Domain Embedding Framework for Robust Reversible Data Hiding Scheme in Encrypted Videos." Electronics 11, no. 16 (2022): 2552. http://dx.doi.org/10.3390/electronics11162552.

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For easier cloud management, reversible data hiding is performed in an encrypted domain to embed label information. However, the existing schemes are not robust and may cause the loss of label information during transmission. Enhancing robustness while maintaining reversibility in data hiding is a challenge. In this paper, a multi-domain embedding framework in encrypted videos is proposed to achieve both robustness and reversibility. In the framework, the multi-domain characteristic of encrypted video is fully used. The element for robust embedding is encrypted through Logistic chaotic scrambl

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Chen, Ze-Sen, Xuan Wu, Qing-Guo Chen, Yao Hu, and Min-Ling Zhang. "Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3553–60. http://dx.doi.org/10.1609/aaai.v34i04.5761.

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In multi-view multi-label learning (MVML), each training example is represented by different feature vectors and associated with multiple labels simultaneously. Nonetheless, the labeling quality of training examples is tend to be affected by annotation noises. In this paper, the problem of multi-view partial multi-label learning (MVPML) is studied, where the set of associated labels are assumed to be candidate ones and only partially valid. To solve the MVPML problem, a two-stage graph-based disambiguation approach is proposed. Firstly, the ground-truth labels of each training example are esti

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Vanegas, Jorge A., Hugo Jair Escalante, and Fabio A. González. "Scalable multi-label annotation via semi-supervised kernel semantic embedding." Pattern Recognition Letters 123 (May 2019): 97–103. http://dx.doi.org/10.1016/j.patrec.2018.10.001.

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Liu, Shuang, XunQin Chen, Peng Chen, and Simon Kolmanič. "Label-Guided relation prototype generation for Continual Relation Extraction." PeerJ Computer Science 10 (October 8, 2024): e2327. http://dx.doi.org/10.7717/peerj-cs.2327.

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Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data. To address the problem of catastrophic forgetting, some existing research endeavors have focused on exploring memory replay methods by storing typical historical learned instances or embedding all observed relations as prototypes by averaging the hidden representation of samples and replaying them in the subsequent training process. However, this prototype generation method overlooks the rich semantic information within the label namespace and are also constrained by memory s

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Kirchoff, Kathryn E., and Shawn M. Gomez. "EMBER: multi-label prediction of kinase-substrate phosphorylation events through deep learning." Bioinformatics 38, no. 8 (2022): 2119–26. http://dx.doi.org/10.1093/bioinformatics/btac083.

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Abstract Motivation Kinase-catalyzed phosphorylation of proteins forms the backbone of signal transduction within the cell, enabling the coordination of numerous processes such as the cell cycle, apoptosis, and differentiation. Although on the order of 105 phosphorylation events have been described, we know the specific kinase performing these functions for &lt;5% of cases. The ability to predict which kinases initiate specific individual phosphorylation events has the potential to greatly enhance the design of downstream experimental studies, while simultaneously creating a preliminary ma

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Lin, Yi, and Honggang Zhang. "Regularized Instance Embedding for Deep Multi-Instance Learning." Applied Sciences 10, no. 1 (2019): 64. http://dx.doi.org/10.3390/app10010064.

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In the era of Big Data, multi-instance learning, as a weakly supervised learning framework, has various applications since it is helpful to reduce the cost of the data-labeling process. Due to this weakly supervised setting, learning effective instance representation/embedding is challenging. To address this issue, we propose an instance-embedding regularizer that can boost the performance of both instance- and bag-embedding learning in a unified fashion. Specifically, the crux of the instance-embedding regularizer is to maximize correlation between instance-embedding and underlying instance-l

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Park, Youngki, and Youhyun Shin. "Applying Object Detection and Embedding Techniques to One-Shot Class-Incremental Multi-Label Image Classification." Applied Sciences 13, no. 18 (2023): 10468. http://dx.doi.org/10.3390/app131810468.

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In this paper, we introduce an efficient approach to multi-label image classification that is particularly suited for scenarios requiring rapid adaptation to new classes with minimal training data. Unlike conventional methods that rely solely on neural networks trained on known classes, our model integrates object detection and embedding techniques to allow for the fast and accurate classification of novel classes based on as few as one sample image. During training, we use either Convolutional Neural Network (CNN)- or Vision Transformer-based algorithms to convert the provided sample images o

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Hou, Yutai, Yongkui Lai, Yushan Wu, Wanxiang Che, and Ting Liu. "Few-shot Learning for Multi-label Intent Detection." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 13036–44. http://dx.doi.org/10.1609/aaai.v35i14.17541.

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In this paper, we study the few-shot multi-label classification for user intent detection. For multi-label intent detection, state-of-the-art work estimates label-instance relevance scores and uses a threshold to select multiple associated intent labels. To determine appropriate thresholds with only a few examples, we first learn universal thresholding experience on data-rich domains, and then adapt the thresholds to certain few-shot domains with a calibration based on nonparametric learning. For better calculation of label-instance relevance score, we introduce label name embedding as anchor

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Zeng, Peng, Shixuan Lin, Hao Sun, and Dongbo Zhou. "Exploiting Hierarchical Label Information in an Attention-Embedding, Multi-Task, Multi-Grained, Network for Scene Classification of Remote Sensing Imagery." Applied Sciences 12, no. 17 (2022): 8705. http://dx.doi.org/10.3390/app12178705.

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Remote sensing scene classification aims to automatically assign proper labels to remote sensing images. Most of the existing deep learning based methods usually consider the interclass and intraclass relationships of the image content for classification. However, these methods rarely consider the hierarchical information of scene labels, as a scene label may belong to hierarchically multi-grained levels. For example, multi-grained level labels may indicate that a remote sensing scene image may belong to the coarse-grained label “transportation land” while also belonging to the fine-grained la

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Wang, Kaixiang. "Robust Cross-View Embedding With Discriminant Structure for Multi-Label Classification." IEEE Access 9 (2021): 117596–607. http://dx.doi.org/10.1109/access.2021.3106680.

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Ma, Yinglong, Xiaofeng Liu, Lijiao Zhao, Yue Liang, Peng Zhang, and Beihong Jin. "Hybrid embedding-based text representation for hierarchical multi-label text classification." Expert Systems with Applications 187 (January 2022): 115905. http://dx.doi.org/10.1016/j.eswa.2021.115905.

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Chen, Zijie, and Zhifeng Hao. "A unified multi-label classification framework with supervised low-dimensional embedding." Neurocomputing 171 (January 2016): 1563–75. http://dx.doi.org/10.1016/j.neucom.2015.07.087.

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Ma, Ao, Fuming You, Mengmeng Jing, Jingjing Li, and Ke Lu. "Multi-source domain adaptation with graph embedding and adaptive label prediction." Information Processing & Management 57, no. 6 (2020): 102367. http://dx.doi.org/10.1016/j.ipm.2020.102367.

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Su, Shuzhi, Hongwei Ge, and Yun-Hao Yuan. "A label embedding kernel method for multi-view canonical correlation analysis." Multimedia Tools and Applications 76, no. 12 (2016): 13785–803. http://dx.doi.org/10.1007/s11042-016-3786-3.

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Zhang, Min-Ling, Jun-Peng Fang, and Yi-Bo Wang. "BiLabel-Specific Features for Multi-Label Classification." ACM Transactions on Knowledge Discovery from Data 16, no. 1 (2021): 1–23. http://dx.doi.org/10.1145/3458283.

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In multi-label classification, the task is to induce predictive models which can assign a set of relevant labels for the unseen instance. The strategy of label-specific features has been widely employed in learning from multi-label examples, where the classification model for predicting the relevancy of each class label is induced based on its tailored features rather than the original features. Existing approaches work by generating a group of tailored features for each class label independently, where label correlations are not fully considered in the label-specific features generation proce

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Akbar, Jihadul, Hairul Fahmi, and Wafiah Murniati. "MULTI LABEL KLASIFIKASI GENRE FILM BERDASARKAN SINOPSIS MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM)." Jurnal Manajemen Informatika dan Sistem Informasi 8, no. 1 (2025): 110–19. https://doi.org/10.36595/misi.v8i1.1436.

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Film merupakan sarana hiburan yang dapat dinikmati oleh banyak orang, bukan hanya sebagai hiburan tetapi juga merupakan sarana pemasaran, perdagangan dan pendidikan. Genre merupakan salah satu karakteristik penting dari sebuah film. Oleh sebab itu klasifikasi genre merupakan cara untuk menemukan hubungan dari masing-masing film sehingga memudahkan penonton untuk menemukan film yang sesuai. Klasifikasi genre film mungkin sangat komprehensif atau beragam berdasarkan kriteria, ada banyak genre yang serupa dalam satu film mungkin termasuk beberapa genre di dalamnya. Untuk menyelesaikan masalah ter

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Zhang, Jiarong, Jinsha Yuan, Jing Zhang, Zhihong Luo, and Aitong Li. "Multi-Meta Information Embedding Enhanced BERT for Chinese Mechanics Entity Recognition." Applied Sciences 13, no. 20 (2023): 11325. http://dx.doi.org/10.3390/app132011325.

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The automatic extraction of key entities in mechanics problems is an important means to automatically solve mechanics problems. Nevertheless, for standard Chinese, compared with the open domain, mechanics problems have a large number of specialized terms and composite entities, which leads to a low recognition capability. Although recent research demonstrates that external information and pre-trained language models can improve the performance of Chinese Named Entity Recognition (CNER), few efforts have been made to combine the two to explore high-performance algorithms for extracting mechanic

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Wang, Zhen, Yiqun Duan, Liu Liu, and Dacheng Tao. "Multi-label Few-shot Learning with Semantic Inference (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15917–18. http://dx.doi.org/10.1609/aaai.v35i18.17955.

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Few-shot learning can adapt the classification model to new labels with only a few labeled examples. Previous studies mainly focus on the scenario of a single category label per example but have not solved the more challenging multi-label scenario with exponential-sized output space and low-data effectively. In this paper, we propose a semantic-aware meta-learning model for multi-label few-shot learning. Our approach can learn and infer the semantic correlation between unseen labels and historical labels to quickly adapt multi-label tasks from only a few examples. Specifically, features can be

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Xia, Pan, Zhongrui Bai, Yicheng Yao, et al. "Advanced Deep Neural Network with Unified Feature-Aware and Label Embedding for Multi-Label Arrhythmias Classification." Tsinghua Science and Technology 30, no. 3 (2025): 1251–69. https://doi.org/10.26599/tst.2023.9010162.

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Wang, Xiao-dong, Rung-Ching Chen, Chao-qun Hong, Zhi-qiang Zeng, and Zhi-li Zhou. "Semi-supervised multi-label feature selection via label correlation analysis with l 1 -norm graph embedding." Image and Vision Computing 63 (July 2017): 10–23. http://dx.doi.org/10.1016/j.imavis.2017.05.004.

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Ashraf, Noman, Lal Khan, Sabur Butt, Hsien-Tsung Chang, Grigori Sidorov, and Alexander Gelbukh. "Multi-label emotion classification of Urdu tweets." PeerJ Computer Science 8 (April 22, 2022): e896. http://dx.doi.org/10.7717/peerj-cs.896.

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Urdu is a widely used language in South Asia and worldwide. While there are similar datasets available in English, we created the first multi-label emotion dataset consisting of 6,043 tweets and six basic emotions in the Urdu Nastalíq script. A multi-label (ML) classification approach was adopted to detect emotions from Urdu. The morphological and syntactic structure of Urdu makes it a challenging problem for multi-label emotion detection. In this paper, we build a set of baseline classifiers such as machine learning algorithms (Random forest (RF), Decision tree (J48), Sequential minimal optim

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Ni, Tongguang, Yan Ding, Jing Xue, Kaijian Xia, Xiaoqing Gu, and Yizhang Jiang. "Local Constraint and Label Embedding Multi-layer Dictionary Learning for Sperm Head Classification." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 3s (2021): 1–16. http://dx.doi.org/10.1145/3458927.

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Morphological classification of human sperm heads is a key technology for diagnosing male infertility. Due to its sparse representation and learning capability, dictionary learning has shown remarkable performance in human sperm head classification. To promote the discriminability of the classification model, a novel local constraint and label embedding multi-layer dictionary learning model called LCLM-MDL is proposed in this study. Based on the multi-layer dictionary learning framework, two dictionaries are built on the basis of Laplacian regularized constraint and label embedding term in eac

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