Results 11 to 20 of about 50 (50)
Relation Extraction with Contextualized Relation Embedding (CRE) [PDF]
Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, 2020 Relation extraction is the task of identifying relation instance between two entities given a corpus whereas Knowledge base modeling is the task of representing a knowledge base, in terms of relations between entities. This paper proposes an architecture for the relation extraction task that integrates semantic information with knowledge base modeling ...
Rohan Badlani, Xiaoyu Chen
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Unsupervised Open Relation Extraction [PDF]
, 2017 We explore methods to extract relations between named entities from free text in an unsupervised setting. In addition to standard feature extraction, we develop a novel method to re-weight word embeddings. We alleviate the problem of features sparsity using an individual feature reduction.
Elsahar, Hady +4 more
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Incorporating Relation Paths in Neural Relation Extraction [PDF]
Proceedings of the 2017 Conference on Empirical Methods in Natural
Language Processing, 2017 Distantly supervised relation extraction has been widely used to find novel relational facts from plain text. To predict the relation between a pair of two target entities, existing methods solely rely on those direct sentences containing both entities.
Yankai Lin +3 more
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Composite kernels for relation extraction [PDF]
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers on - ACL-IJCNLP '09, 2009 The automatic extraction of relations between entities expressed in natural language text is an important problem for IR and text understanding. In this paper we show how different kernels for parse trees can be combined to improve the relation extraction quality.
Hannes Korte +2 more
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Datasets for generic relation extraction [PDF]
Natural Language Engineering, 2011 AbstractA vast amount of usable electronic data is in the form of unstructured text. The relation extraction task aims to identify useful information in text (e.g. PersonW works for OrganisationX, GeneY encodes ProteinZ) and recode it in a format such as a relational database or RDF triplestore that can be more effectively used for querying and ...
Hachey, B., Grover, C., Tobin, R.
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Predicting Document Coverage for Relation Extraction
Transactions of the Association for Computational Linguistics, 2022 Abstract This paper presents a new task of predicting the coverage of a text document for relation extraction (RE): Does the document contain many relational tuples for a given entity? Coverage predictions are useful in selecting the best documents for knowledge base construction with large input corpora.
Singhania, S. +2 more
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Learning Relational Dependency Networks for Relation Extraction [PDF]
, 2017 We consider the task of KBP slot filling -- extracting relation information from newswire documents for knowledge base construction. We present our pipeline, which employs Relational Dependency Networks (RDNs) to learn linguistic patterns for relation extraction.
Soni, Ameet +3 more
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Self-Crowdsourcing Training for Relation Extraction [PDF]
Proceedings of the 55th Annual Meeting of the Association for
Computational Linguistics (Volume 2: Short Papers), 2017 In this paper we introduce a self-training strategy for crowdsourcing. The training examples are automatically selected to train the crowd workers. Our experimental results show an impact of 5% Improvement in terms of F1 for relation extraction task, compared to the method based on distant supervision.
Azad Abad +2 more
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Learning to extract relations for protein annotation [PDF]
Bioinformatics, 2007 Abstract Motivation: Protein annotation is a task that describes protein X in terms of topic Y. Usually, this is constructed using information from the biomedical literature. Until now, most of literature-based protein annotation work has been done manually by human annotators.
Kim, Jee-Hyub +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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