Results 11 to 20 of about 292 (213)
Visual Distant Supervision for Scene Graph Generation [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 Scene graph generation aims to identify objects and their relations in images, providing structured image representations that can facilitate numerous applications in computer vision. However, scene graph models usually require supervised learning on large quantities of labeled data with intensive human annotation.
Yuan Yao 0011 +7 more
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Distant Supervision Relation Extraction Based on Focal Loss and Residual Network [PDF]
Jisuanji gongcheng, 2019 Distant supervision relation extraction based on Convolutional Neural Network(CNN) can extract only single feature,and the standard cross-entropy loss function is not sufficient in balancing the ratio of positive samples and negative samples in datasets ...
CAI Qiang, LI Jing, HAO Jiayun
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Biomedical Relation Extraction Using Distant Supervision [PDF]
Scientific Programming, 2020 With the accelerating growth of big data, especially in the healthcare area, information extraction is more needed currently than ever, for it can convey unstructured information into an easily interpretable structured data. Relation extraction is the second of the two important tasks of relation extraction.
Nada Boudjellal +3 more
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Improving Distantly-Supervised Relation Extraction Through BERT-Based Label and Instance Embeddings
IEEE Access, 2021 Distantly-supervised relation extraction (RE) is an effective method to scale RE to large corpora but suffers from noisy labels. Existing approaches try to alleviate noise through multi-instance learning and by providing additional information but manage
Despina Christou, Grigorios Tsoumakas
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Distant supervision for medical concept normalization
Journal of Biomedical Informatics, 2020 We consider the task of Medical Concept Normalization (MCN) which aims to map informal medical phrases such as "loosing weight" to formal medical concepts, such as "Weight loss". Deep learning models have shown high performance across various MCN datasets containing small number of target concepts along with adequate number of training examples per ...
Nikhil Pattisapu +4 more
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EANT: Distant Supervision for Relation Extraction with Entity Attributes via Negative Training
Applied Sciences, 2022 Distant supervision for relation extraction (DSRE) automatically acquires large-scale annotated data by aligning the corpus with the knowledge base, which dramatically reduces the cost of manual annotation.
Xuxin Chen, Xinli Huang
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Bridging social media via distant supervision [PDF]
Social Network Analysis and Mining, 2015 Microblog classification has received a lot of attention in recent years. Different classification tasks have been investigated, most of them focusing on classifying microblogs into a small number of classes (five or less) using a training set of manually annotated tweets.
Walid Magdy +3 more
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Distant Supervision for Entity Linking
CoRR, 2015 Entity linking is an indispensable operation of populating knowledge repositories for information extraction. It studies on aligning a textual entity mention to its corresponding disambiguated entry in a knowledge repository. In this paper, we propose a new paradigm named distantly supervised entity linking (DSEL), in the sense that the disambiguated ...
Miao Fan, Qiang Zhou, Thomas Fang Zheng
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Distant Supervision Relation Extraction Combining Attention Mechanism and Ontology
Jisuanji kexue yu tansuo, 2020 Relational extraction extracts relationships from unstructured text and outputs them in a structured form. In order to improve the extraction accuracy and reduce the dependence on manual annotation, this paper proposes a distant supervision relationship ...
LI Yanjuan, ZANG Mingzhe, LIU Xiaoyan, LIU Yang, GUO Maozu
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Distant Supervision for Sentiment Attitude Extraction
Proceedings - Natural Language Processing in a Deep Learning World, 2019 © 2019 Association for Computational Linguistics (ACL). All rights reserved. News articles often convey attitudes between the mentioned subjects, which is essential for understanding the described situation. In this paper, we describe a new approach to distant supervision for extracting sentiment attitudes between named entities mentioned in texts. Two
Nicolay Rusnachenko +2 more
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