Results 31 to 40 of about 292 (213)
Semi-supervised Stance Detection of Tweets Via Distant Network Supervision [PDF]
Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, 2022 Detecting and labeling stance in social media text is strongly motivated by hate speech detection, poll prediction, engagement forecasting, and concerted propaganda detection. Today's best neural stance detectors need large volumes of training data, which is difficult to curate given the fast-changing landscape of social media text and issues on which ...
Subhabrata Dutta +3 more
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Distantly Supervised Named Entity Recognition with Self-Adaptive Label Correction
Applied Sciences, 2022 Named entity recognition has achieved remarkable success on benchmarks with high-quality manual annotations. Such annotations are labor-intensive and time-consuming, thus unavailable in real-world scenarios.
Binling Nie, Chenyang Li
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Mapping ESG Trends by Distant Supervision of Neural Language Models
Machine Learning and Knowledge Extraction, 2020 The integration of Environmental, Social and Governance (ESG) considerations into business decisions and investment strategies have accelerated over the past few years. It is important to quantify the extent to which ESG-related conversations are carried
Natraj Raman +2 more
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Relation Extraction Using Distant Supervision
ACM Computing Surveys, 2018 Relation extraction is a subtask of information extraction where semantic relationships are extracted from natural language text and then classified. In essence, it allows us to acquire structured knowledge from unstructured text.
Alisa Smirnova, Philippe Cudré-Mauroux
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Improving distant supervision using inference learning [PDF]
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), 2015 Distant supervision is a widely applied approach to automatic training of relation extraction systems and has the advantage that it can generate large amounts of labelled data with minimal effort. However, this data may contain errors and consequently systems trained using distant supervision tend not to perform as well as those based on manually ...
Roland Roller +3 more
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Adaptive Named Entity Recognition Using Distant Supervision for Contemporary Written Texts
IEEE Access, 2021 Named entity recognition (NER) is the process of categorizing named entities in a given text that suffers from the lack of labeled corpora, which is a long-standing issue. Deep neural networks have been successfully applied to NER tasks.
Juae Kim +3 more
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Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation [PDF]
Proceedings of the 28th International Conference on Computational Linguistics, 2020 Relation extraction (RE) has been extensively studied due to its importance in real-world applications such as knowledge base construction and question answering. Most of the existing works train the models on either distantly supervised data or human-annotated data.
Woohwan Jung, Kyuseok Shim
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Multi-factor person entity relation extraction model based on distant supervision
Tongxin xuebao, 2018 Aiming at the problem that the basic assumption of distant supervision was too strong and easy to produce noise data,a model of the person entity relation extraction which could automatically filter the training data generated by distant supervision was ...
Yangchen HUANG +5 more
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Spatial relation learning in complementary scenarios with deep neural networks
Frontiers in Neurorobotics, 2022 A cognitive agent performing in the real world needs to learn relevant concepts about its environment (e.g., objects, color, and shapes) and react accordingly.
Jae Hee Lee +6 more
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International Journal of Digital Earth, 2022 Earth observations, especially satellite data, have produced a wealth of methods and results in meeting global challenges, often presented in unstructured texts such as papers or reports.
Ming Lin, Meng Jin, Yufu Liu, Yuqi Bai
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