Results 11 to 20 of about 745,152 (307)
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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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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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
Qinxue Meng +3 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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Global Relation Embedding for Relation Extraction [PDF]
Proceedings of the 2018 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language
Technologies, Volume 1 (Long Papers), 2018 We study the problem of textual relation embedding with distant supervision. To combat the wrong labeling problem of distant supervision, we propose to embed textual relations with global statistics of relations, i.e., the co-occurrence statistics of textual and knowledge base relations collected from the entire corpus.
Yu Su 0001 +5 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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Relation Extraction Using Semantic Information
Computational Linguistics, 2023 14th International Conference of the Pacific Association for Computational Linguistics, PACLING 2015, Bali, Indonesia, May 19-21, 2015Research works on relation extraction have put a lot of attention on finding features of surface text and syntactic ...
Jian Xu, Qin Lu, Minglei Li
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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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