Heterogeneous domain adaptation with label and structural consistency

Huang, Junchu; Zhou, Zhiheng; Shang, Junyuan; Niu, Chang

doi:10.1007/s11042-020-08731-x

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…Huang et al. ( 2020b ) outperformed several baseline adaptation methods even if the distribution difference was substantially large. Kang et al.…”

Section: Results Analysis Per Datasetsmentioning

confidence: 99%

“…In cross-lingual text categorization, Huang et al. ( 2020b ) elaborated a novel algorithm denominated heterogeneous discriminative features learning and LP to learn discriminative features with label consistency through two domain-specific projections, and LP through exploiting structural information of data.…”

Section: Semi-supervised Learning For Text Classificationmentioning

confidence: 99%

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A review of semi-supervised learning for text classification

Duarte

Berton

2023

Artif Intell Rev

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A huge amount of data is generated daily leading to big data challenges. One of them is related to text mining, especially text classification. To perform this task we usually need a large set of labeled data that can be expensive, time-consuming, or difficult to be obtained. Considering this scenario semi-supervised learning (SSL), the branch of machine learning concerned with using labeled and unlabeled data has expanded in volume and scope. Since no recent survey exists to overview how SSL has been used in text classification, we aim to fill this gap and present an up-to-date review of SSL for text classification. We retrieve 1794 works from the last 5 years from IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Then, 157 articles were selected to be included in this review. We present the application domain, datasets, and languages employed in the works. The text representations and machine learning algorithms. We also summarize and organize the works following a recent taxonomy of SSL. We analyze the percentage of labeled data used, the evaluation metrics, and obtained results. Lastly, we present some limitations and future trends in the area. We aim to provide researchers and practitioners with an outline of the area as well as useful information for their current research.

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“…Huang et al. ( 2020b ) outperformed several baseline adaptation methods even if the distribution difference was substantially large. Kang et al.…”

Section: Results Analysis Per Datasetsmentioning

confidence: 99%

Section: Semi-supervised Learning For Text Classificationmentioning

confidence: 99%

A review of semi-supervised learning for text classification

Duarte

Berton

2023

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…In contrast, our solution is concise with one simple objective of cross-domain structure preserving. Recently, Huang et al [14] proposed a novel algorithm, named heterogeneous discriminative features learning and label propagation (HDL). This algorithm is similar to ours in that both tend to preserve structure information in the learned common subspace.…”

Section: Common Subspace Learningmentioning

confidence: 99%