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A survey on Relation Extraction
Intelligent Systems with Applications, 2023 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.
Kartik Detroja +2 more
doaj +2 more sources
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 ...
Gur, Izzeddin +5 more
core +2 more sources
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).
Ananiadou, Sophia +2 more
core +2 more sources
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)
Klöser, Lars +3 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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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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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
doaj +1 more source
Review of Text-Oriented Entity Relation Extraction Research [PDF]
Jisuanji kexue yu tansuoInformation extraction is the foundation of knowledge graph construction, and relation extraction, as a key process and core step of information extraction, aims to locate entities from text data and recognize semantic links between entities.
REN Anqi, LIU Lin, WANG Hailong, LIU Jing
doaj +1 more source
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.
Shahbazi, Hamed +3 more
openaire +2 more sources
Handling Uncertainty in Relation Extraction [PDF]
Proceedings of the 8th International Conference on Knowledge Capture, 2015 Relation extraction involves different types of uncertainty due to the imperfection of the extraction tools and the inherent ambiguity of unstructured text. In this paper, we discuss several ways of handling uncertainties in relation extraction from social media.
Verburg, Jochem +2 more
openaire +2 more sources