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MCEEGNet: a multi-cue EEG network for quantitative assessment of depression using emotional stimuli-induced EEG signals. [PDF]
Front Artif IntellDing X, Chen X, Xu M, Zhang Y, Chang H.
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A novel reasoning mechanism for multi-label text classification
Information Processing and Management, 2021Abstract The aim in multi-label text classification is to assign a set of labels to a given document. Previous classifier-chain and sequence-to-sequence models have been shown to have a powerful ability to capture label correlations. However, they rely heavily on the label order, while labels in multi-label data are essentially an unordered set.
Weiguang Qu
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Metalearning Applied to Multi-label Text Classification
XVI Brazilian Symposium on Information Systems, 2020Data Mining and Machine Learning fields have many techniques that can support data analysts in the text classification task. However, finding the most adequate techniques require advanced technical knowledge, exhaustive computational experiments and, consequently, time. To address this issue, researchers have proposed different approaches for selecting
Vânia Batista dos Santos +1 more
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Label-Aware Text Representation for Multi-Label Text Classification
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021Multi-label text classification (MLTC) is an important task in natural language processing (NLP), which is appealing to researchers in both academia and industry. However, few of studies have been conducted on the relations among the labels. Most of existing methods tend to neglect the semantic information between labels and words.
Hao Guo +4 more
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Image to Text Translation by Multi-Label Classification
2010This paper presents an image to text translation platform consisting of image segmentation, region features extraction, region blobs clustering, and translation components. Different multi-label learning method is suggested for realizing the translation component.
Gulisong Nasierding, Abbas Z. Kouzani
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Multi-label Classification of Legislative Text into EuroVoc
2012In this paper we present a novel method for the automatic classification of multi-label text documents. In principle, automatic classification of text is usually tackled by supervised Machine Learning techniques like Support Vector Machines (SVM), that typically achieve state-of-the-art accuracy in several domains.
Guido Boella +4 more
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Correlation Networks for Extreme Multi-label Text Classification
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020This paper develops the Correlation Networks (CorNet) architecture for the extreme multi-label text classification (XMTC) task, where the objective is to tag an input text sequence with the most relevant subset of labels from an extremely large label set. XMTC can be found in many real-world applications, such as document tagging and product annotation.
Guangxu Xun +3 more
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Reinforcement Learning for Extreme Multi-label Text Classification
2021Extreme multi-label text classification (XMC) is an important yet challenging problem in the NLP community, which refers to the problem of assigning to each document its most relevant subset of class labels from an extremely large label collection. For example, the input text could be a story document on chinastory.cn and the labels could be story ...
Hui Teng +4 more
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A Neural Architecture for Multi-label Text Classification
2018We propose a novel supervised approach for multi-label text classification, which is based on a neural network architecture consisting of a single encoder and multiple classifier heads. Our method predicts which subset of possible tags best matches an input text.
Sam Coope +6 more
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