Results 1 to 10 of about 50 (50)
Multi-Attribute Relation Extraction (MARE): Simplifying the Application of Relation Extraction [PDF]
Proceedings of the 2nd International Conference on Deep Learning Theory and Applications, 2021 Preprint of short paper for the 2nd International Conference on Deep Learning Theory and Applications (2021)
Bodo Kraft +3 more
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Relation Extraction with Explanation [PDF]
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020 Recent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences. Efforts thus far have focused on improving extraction accuracy but little is known about their explainability.
Prasad Tadepalli +3 more
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A survey on Relation Extraction
Intelligent Systems with Applications, 2022 With the advent of the Internet, the daily production of digital text in the form of social media, emails, blogs, news items, books, research papers, and Q&A forums has increased significantly. This unstructured or semi-structured text contains a huge amount of information.
Kartik Detroja +2 more
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Neural relation extraction: a review [PDF]
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2021 Neural relation extraction discovers semantic relations between entities from unstructured text using deep learning methods. In this study, we make a clear categorization of the existing relation extraction methods in terms of data expressiveness and data supervision, and present a comprehensive and comparative review.
Mehmet AYDAR, Özge BOZAL, Furkan ÖZBAY
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Enriching Relation Extraction with OpenIE
Proceedings of the 12th International Conference on Data Science, Technology and Applications, 2023 Relation extraction (RE) is a sub-discipline of information extraction (IE) which focuses on the prediction of a relational predicate from a natural-language input unit (such as a sentence, a clause, or even a short paragraph consisting of multiple sentences and/or clauses).
Temperoni, Alessandro +2 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.
Xifeng Yan +5 more
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Extracting Relations Between Sectors
2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), 2022 The term "sector" in professional business life is a vague concept since companies tend to identify themselves as operating in multiple sectors simultaneously. This ambiguity poses problems in recommending jobs to job seekers or finding suitable candidates for open positions.
Daniş, Fahri Serhan +3 more
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Dialogue-Based Relation Extraction [PDF]
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020 To appear in ACL ...
Dian Yu, Kai Sun, Dong Yu, Claire Cardie
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Revisiting Unsupervised Relation Extraction [PDF]
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020 Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be categorised into generative and discriminative approaches, which rely either on hand-crafted features or surface form.
Sophia Ananiadou, Thy Thy Tran, Phong Le
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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
David B. Skillicorn +3 more
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