Results 21 to 30 of about 298 (213)
IEEE Access, 2020 Recent advances in deep neural networks (DNNs) have enabled us to achieve reliable named entity recognition (NER) models without handcrafting features. However, these are also some obstacles imposed by using those machine learning methods, in need of a ...
Dezheng Zhang +6 more
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Distant Supervision from Knowledge Graphs [PDF]
, 2018 In this chapter, we discuss approaches leveraging distant supervision for relation extraction. We start by introducing the key ideas behind distant supervision as well as their main shortcomings. We then discuss approaches that improve over the basic method, including approaches based on the at-least-one-principle along with their extensions for ...
Smirnova, Alisa +2 more
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Jointly Embedding Entities and Text with Distant Supervision [PDF]
Proceedings of The Third Workshop on Representation Learning for NLP, 2018 12 pages; Accepted to 3rd Workshop on Representation Learning for NLP (Repl4NLP 2018).
Newman-Griffis, D. +2 more
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Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 The black-box nature of neural models has motivated a line of research that aims to generate natural language rationales to explain why a model made certain predictions. Such rationale generation models, to date, have been trained on dataset-specific crowdsourced rationales, but this approach is costly and is not generalizable to new tasks and domains.
Faeze Brahman +3 more
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Chemical-induced disease relation extraction via attention-based distant supervision
BMC Bioinformatics, 2019 Background Automatically understanding chemical-disease relations (CDRs) is crucial in various areas of biomedical research and health care. Supervised machine learning provides a feasible solution to automatically extract relations between biomedical ...
Jinghang Gu +3 more
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Boosting Knowledge Base Automatically via Few-Shot Relation Classification
Frontiers in Neurorobotics, 2020 Relation classification (RC) aims at extracting structural information, i.e., triplets of two entities with a relation, from free texts, which is pivotal for automatic knowledge base construction. In this paper, we investigate a fully automatic method to
Ning Pang +3 more
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An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction
Information, 2020 Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to
Xiaoyu Han +3 more
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Iterative Annotation of Biomedical NER Corpora with Deep Neural Networks and Knowledge Bases
Applied Sciences, 2022 The large availability of clinical natural language documents, such as clinical narratives or diagnoses, requires the definition of smart automatic systems for their processing and analysis, but the lack of annotated corpora in the biomedical domain ...
Stefano Silvestri +2 more
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Information Extraction Using Distant Supervision and Semantic Similarities
Advances in Electrical and Computer Engineering, 2016 Information extraction is one of the main research tasks in natural language processing and text mining that extracts useful information from unstructured sentences. Information extraction techniques include named entity recognition, relation extraction,
PARK, Y., KANG, S., SEO, J.
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Distantly Supervised Named Entity Recognition with Self-Adaptive Label Correction
Applied Sciences, 2022 Named entity recognition has achieved remarkable success on benchmarks with high-quality manual annotations. Such annotations are labor-intensive and time-consuming, thus unavailable in real-world scenarios.
Binling Nie, Chenyang Li
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