Results 81 to 90 of about 5,797,629 (324)
Neural Temporal Relation Extraction [PDF]
We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-of-the-art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with ...
Dmitriy Dligach +4 more
openaire +1 more source
Chinese relation extraction for constructing satellite frequency and orbit knowledge graph: A survey
As Satellite Frequency and Orbit (SFO) constitute scarce natural resources, constructing a Satellite Frequency and Orbit Knowledge Graph (SFO-KG) becomes crucial for optimizing their utilization.
Yuanzhi He, Zhiqiang Li, Zheng Dou
doaj +1 more source
Unsupervised Open Relation Extraction [PDF]
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
openaire +2 more sources
Drug–Drug Interaction Relation Extraction Based on Deep Learning: A Review
Drug–drug interaction (DDI) is an important part of drug development and pharmacovigilance. At the same time, DDI is an important factor in treatment planning, monitoring effects of medicine and patient safety, and has a significant impact on public ...
Mingliang Dou +4 more
semanticscholar +1 more source
Communication and Language Profiles of Children Treated for Posterior Fossa Brain Tumors
ABSTRACT Background Cognitive and language deficits are frequently reported sequelae of posterior fossa brain tumors (PFBT). Typically, delayed onset impedes prompt assessment and early intervention. This has devastating implications for quality of life.
Zara Sved +4 more
wiley +1 more source
Improved Relation Extraction with Feature-Rich Compositional Embedding Models
Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes ...
Dredze, Mark +2 more
core +1 more source
ABSTRACT Purpose Patient activation—encompassing knowledge, confidence, and skills in managing individual's health—is a cornerstone of person‐centered care. However, its significance among childhood, adolescent, and young adult cancer survivors (CAYACS) remains unexplored. This article examines the application of the 13‐item Patient Activation Measure (
Charlotte Demoor‐Goldschmidt +12 more
wiley +1 more source
Relation extraction based on CNN and Bi-LSTM
Relation extraction aims to identify the entities in the Web text and extract the implicit relationships between entities in the text.Studies have shown that deep neural networks are feasible for relation extraction tasks and are superior to traditional ...
Xiaobin ZHANG,Fucai CHEN,Ruiyang HUANG
doaj +1 more source
Multi-labeled Relation Extraction with Attentive Capsule Network
To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly
Jia, Weijia +3 more
core +1 more source
ABSTRACT Asymptomatic infection poses a significant risk for children undergoing hematopoietic stem cell transplantation (HSCT). Pre‐transplant surveillance computed tomography (CT) is commonly used to identify occult infection, though its diagnostic yield remains uncertain.
Tyler Obermark +9 more
wiley +1 more source

