Results 11 to 20 of about 1,302,763 (303)
Joint extraction of entities and relations by entity role recognition
Cognitive Robotics, 2022 Joint extracting entities and relations from unstructured text is a fundamental task in information extraction and a key step in constructing large knowledge graphs, entities and relations are constructed as relational triples of the form (subject ...
Xi Han, Qi-Ming Liu
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A Reinforcement Learning-Based Generative Approach for Event Temporal Relation Extraction. [PDF]
Entropy (Basel)Wu Z, Yang W, Zhang M, Wei F, Liu X.
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Entropy, 2023 Implicit discourse relation recognition (IDRR) has long been considered a challenging problem in shallow discourse parsing. The absence of connectives makes such relations implicit and requires much more effort to understand the semantics of the text ...
Zhongyang Fang +5 more
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A Study on Double-Headed Entities and Relations Prediction Framework for Joint Triple Extraction
Mathematics, 2023 Relational triple extraction, a fundamental procedure in natural language processing knowledge graph construction, assumes a crucial and irreplaceable role in the domain of academic research related to information extraction.
Yanbing Xiao +6 more
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Chinese Relation Extraction Using Extend Softword
IEEE Access, 2021 In recent years, many scholars have chosen to use word lexicons to incorporate word information into a model based on character input to improve the performance of Chinese relation extraction (RE). For example, Li et al.
Bo Kong +4 more
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Towards relation extraction from speech
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022 Relation extraction typically aims to extract semantic relationships between entities from the unstructured text. One of the most essential data sources for relation extraction is the spoken language, such as interviews and dialogues. However, the error propagation introduced in automatic speech recognition (ASR) has been ignored in relation extraction,
Wu, Tongtong +6 more
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Exploiting sequence labeling framework to extract document-level relations from biomedical texts
BMC Bioinformatics, 2020 Background Both intra- and inter-sentential semantic relations in biomedical texts provide valuable information for biomedical research. However, most existing methods either focus on extracting intra-sentential relations and ignore inter-sentential ones
Zhiheng Li +5 more
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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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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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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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