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Article
Computer Science, Information Systems
Shizhe Hu et al.
Summary: This paper proposes a novel joint contrastive triple-learning framework for deep multi-view clustering. By incorporating feature-level alignment, commonality, and cluster-level consistency, this framework learns discriminative feature representations for data clustering. Experimental results demonstrate the superiority of the proposed method over state-of-the-art methods on challenging multi-view datasets.
INFORMATION PROCESSING & MANAGEMENT
(2023)
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Summary: In this paper, a novel clustering algorithm, CURE, is proposed to automatically learn cluster weights and effectively utilize the complementary information of multi-view data, enhancing clustering performance.
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(2022)
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Summary: Medical images from multicentres often suffer from domain shift problem. To address this issue, a novel GAN called IB-GAN is proposed to preserve image-objects during cross-domain image-to-image adaptation.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
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Zhiqiang Geng et al.
Summary: This paper proposes a novel target attention convolutional neural network (TACNN) for relation classification task, which fully utilizes word embedding and position embedding information to extract relationships between entity pairs in textual sentences. By applying the target attention mechanism in the context layer of the convolutional neural network model, the influence of the relationship matrix weights of entity pairs in the sentence is enhanced, while the calculation of irrelevant terms is ignored. In experiments, TACNN achieves high F1 scores on two datasets, surpassing the performance of previously available public models.
INFORMATION SCIENCES
(2022)
Article
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Shizhe Hu et al.
Summary: In this study, a novel method for multiview clustering is proposed, which can explore both interfeature correlations and intercluster correlations to improve clustering performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
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Huiyan Wu et al.
Summary: This paper proposes a Joint Entity and Relation Extraction Network with Enhanced Explicit and Implicit Semantic Information (EINET). By introducing explicit semantics from Semantic Role Labeling and using different Bi-directional Long Short-Term Memory networks to encode entities and local contexts, the model achieves competitive results in entity and relation extraction.
APPLIED SCIENCES-BASEL
(2022)
Article
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Hyejin Jang et al.
Summary: This study contributes to developing a deep learning model by analyzing meaningful types in technical information and constructing a technical text dataset with labels. The results of semantic technology relations can serve as a high-quality source for various technology analysis applications, such as technology tree and technology roadmap. In other words, it has the advantage of providing generalizable technical information that is not dependent on a specific analysis purpose.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Computer Science, Information Systems
Xiaofeng Liu et al.
Summary: This paper proposes a multi-granularity sequential network (MGSN) for document-level relation extraction to address the issues of long-distance context dependency and complex semantics, by learning to extract entity relations at both document-level and entity-level information levels.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Computer Science, Information Systems
Ren Li et al.
Summary: This paper presents a two-stage tagging scheme and a lightweight joint extraction neural model based on the entity-first labeling strategy, which outperforms baseline approaches in relation extraction task and achieves competitive entity recognition effect through comprehensive experiments.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Wen Wen et al.
Summary: Most existing methods for relation extraction heavily rely on large-scale annotated data and have low generalization ability, prompting the development of few-shot learning methods. A novel model integrating the Transformer into a prototypical network shows improved relation-level feature extraction and performance in experiments.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Computer Science, Artificial Intelligence
Qian Wan et al.
Summary: This paper proposes a Region-based Hypergraph Network (RHGN) for joint entity and relation extraction, which introduces the concept of regional hypernodes and constructs a region-based relation hypergraph to improve performance. Experimental results demonstrate the superior performance of the model in both entity recognition and relation extraction.
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(2018)
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Fei Li et al.
BMC BIOINFORMATICS
(2017)
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Harsha Gurulingappa et al.
JOURNAL OF BIOMEDICAL INFORMATICS
(2012)
