Results 31 to 40 of about 5,797,629 (324)
IEEE Access, 2022 The third-generation semiconductor materials (TGSMs) is a frontier scientific domain, where researchers need to consult extensive literature for the entity information on materials, devices, preparation methods, and experimental performances, and sort ...
Xiaobo Jiang, Kun He, Borui Yang
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Exploring a Multi-Layered Cross-Genre Corpus of Document-Level Semantic Relations
Information, 2023 This paper introduces a multi-layered cross-genre corpus, annotated for coreference resolution, causal relations, and temporal relations, comprising a variety of genres, from news articles and children’s stories to Reddit posts.
Gregor Williamson +5 more
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Relational autoencoder for feature extraction [PDF]
2017 International Joint Conference on Neural Networks (IJCNN), 2017 Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it fails to consider the relationships of data samples which may affect experimental results of using original and new
Meng, Qinxue +3 more
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Int. J. Medical Informatics, 2023 BACKGROUND Natural Language Processing (NLP) applications have developed over the past years in various fields including its application to clinical free text for named entity recognition and relation extraction.
David Fraile Navarro +6 more
semanticscholar +1 more source
Improving Continual Relation Extraction by Distinguishing Analogous Semantics [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 Continual relation extraction (RE) aims to learn constantly emerging relations while avoiding forgetting the learned relations. Existing works store a small number of typical samples to re-train the model for alleviating forgetting.
Wenzheng Zhao, Yuanning Cui, Wei Hu
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AI Open, 2020 Event is a common but non-negligible knowledge type. How to identify events from texts, extract their arguments, even analyze the relations between different events are important for many applications.
Kang Liu +4 more
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Distant supervision for relation extraction without labeled data
Annual Meeting of the Association for Computational Linguistics, 2009 Modern models of relation extraction for tasks like ACE are based on supervised learning of relations from small hand-labeled corpora. We investigate an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACE ...
Mike D. Mintz +3 more
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A Partition Filter Network for Joint Entity and Relation Extraction [PDF]
Conference on Empirical Methods in Natural Language Processing, 2021 In joint entity and relation extraction, existing work either sequentially encode task-specific features, leading to an imbalance in inter-task feature interaction where features extracted later have no direct contact with those that come first.
Zhiheng Yan +4 more
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Automatic relationship extraction from agricultural text for ontology construction
Information Processing in Agriculture, 2018 In the present era of Big Data the demand for developing efficient information processing techniques for different applications is expanding steadily. One such possible application is automatic creation of ontology.
Neha Kaushik, Niladri Chatterjee
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Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation [PDF]
Findings, 2022 Document-level Relation Extraction (DocRE) is a more challenging task compared to its sentence-level counterpart. It aims to extract relations from multiple sentences at once.
Qingyu Tan +3 more
semanticscholar +1 more source