Relationship extraction ≈ Relationship extraction
View article: BioBERT: a pre-trained biomedical language representation model for biomedical text mining
BioBERT: a pre-trained biomedical language representation model for biomedical text mining Open
Motivation Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature ha…
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End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures Open
We present a novel end-to-end neural model to extract entities and relations between them.Our recurrent neural network based model captures both word sequence and dependency tree substructure information by stacking bidirectional treestruc…
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Neural Relation Extraction with Selective Attention over Instances Open
Distant supervised relation extraction has been widely used to find novel relational facts from text.However, distant supervision inevitably accompanies with the wrong labelling problem, and these noisy data will substantially hurt the per…
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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts Open
The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining. Reformulating tasks as fill-in-the-blanks problems (e.g., cloze tests) is a natural approach for…
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Graph Convolution over Pruned Dependency Trees Improves Relation Extraction Open
Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively, or…
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BioCreative V CDR task corpus: a resource for chemical disease relation extraction Open
Community-run, formal evaluations and manually annotated text corpora are critically important for advancing biomedical text-mining research. Recently in BioCreative V, a new challenge was organized for the tasks of disease named entity re…
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Position-aware Attention and Supervised Data Improve Slot Filling Open
Organized relational knowledge in the form of “knowledge graphs” is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly slowly. This…
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Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme Open
Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem.. Then, based on o…
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A Novel Cascade Binary Tagging Framework for Relational Triple Extraction Open
Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction. However, few existing works excel in solving the overlapping triple problem where multiple relational triples in the same sentenc…
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Fine-Grained Entity Recognition Open
Entity Recognition (ER) is a key component of relation extraction systems and many other natural-language processing applications. Unfortunately, most ER systems are restricted to produce labels from to a small set of entity classes, e.g.,…
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Attention Guided Graph Convolutional Networks for Relation Extraction Open
Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text. However, how to effectively make use of relevant information while ignoring irrelevant information from the dependen…
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Cross-Sentence <i>N</i>-ary Relation Extraction with Graph LSTMs Open
Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. In…
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GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction Open
In this paper, we present GraphRel, an end-to-end relation extraction model which uses graph convolutional networks (GCNs) to jointly learn named entities and relations. In contrast to previous baselines, we consider the interaction betwee…
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Deep learning in clinical natural language processing: a methodical review Open
Objective This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and co…
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A Frustratingly Easy Approach for Entity and Relation Extraction Open
End-to-end relation extraction aims to identify named entities and extract relations between them. Most recent work models these two subtasks jointly, either by casting them in one structured prediction framework, or performing multi-task …
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Structured information extraction from scientific text with large language models Open
Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large …
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TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking Open
Extracting entities and relations from unstructured text has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in identifying overlapping relations with shared entities. Prior works sho…
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Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions Open
Distant supervision for relation extraction is an efficient method to scale relation extraction to very large corpora which contains thousands of relations. However, the existing approaches have flaws on selecting valid instances and lack …
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Entity-Relation Extraction as Multi-Turn Question Answering Open
In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and elations is transformed to the task of identifying answe…
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KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction Open
Recently, prompt-tuning has achieved promising results for specific few-shot classification tasks. The core idea of prompt-tuning is to insert text pieces (i.e., templates) into the input and transform a classification task into a masked l…
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Improved Neural Relation Detection for Knowledge Base Question Answering Open
Relation detection is a core component of many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning which detects KB relations …
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2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records Open
Objective This article summarizes the preparation, organization, evaluation, and results of Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on extraction of adverse drug events (ADEs) from clinical records…
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Question Answering on Freebase via Relation Extraction and Textual Evidence Open
Existing knowledge-based question answering systems often rely on small annotated training data.While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation metho…
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Reasoning with Latent Structure Refinement for Document-Level Relation Extraction Open
Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities. However, effective aggregation of relevant informati…
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Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees Open
We present a novel attention-based recurrent neural network for joint extraction of entity mentions and relations. We show that attention along with long short term memory (LSTM) network can extract semantic relations between entity mentio…
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CoType Open
Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have relied on human-annotated corpora for training and adopted an …
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Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling Open
Document-level relation extraction (RE) poses new challenges compared to its sentence-level counterpart. One document commonly contains multiple entity pairs, and one entity pair occurs multiple times in the document associated with multip…
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Drug-Drug Interaction Extraction via Convolutional Neural Networks Open
Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM)…
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RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information Open
Distantly-supervised Relation Extraction (RE) methods train an extractor by automatically aligning relation instances in a Knowledge Base (KB) with unstructured text. In addition to relation instances, KBs often contain other relevant side…
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Large language models are few-shot clinical information extractors Open
A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes. However, roadblocks have included dataset shift from the general domain and a lack of public clinical corpora and annotat…