In this paper, we argue that discourse representations can be mapped to networks and analyzed by tools provided in network theory so that deep properties of discourse structure are revealed. Two discourse-annotated corpora, C58 and STAC, that belong...
Relation extraction is a type of information extraction task that recognizes semantic relationships between entities in a sentence. Many previous studies have focused on extracting only one semantic relation between two entities in a single sentence....
Maritime traffic can reflect the diverse and complex relations between countries and regions, such as economic trade and geopolitics. Based on the AIS (Automatic Identification System) trajectory data of ships, this study constructs the Maritime Silk...
This paper defines the weighted super adjacency matrix based on the existing super adjacency matrix. This paper, for the first time, combines the trade network, competitive network, and complementary network to construct the trade multilayer network,...
Event–event causal relation extraction (ECRE) represents a critical yet challenging task in natural language processing. Existing studies primarily focus on extracting causal sentences and events, despite the use of joint extraction methods for...
Based on the Theory of Planned Behavior (TPB), this is the first study that integrates Entrepreneurial Policy (EPL) and Entrepreneurial Network Relations (ENR) to examine the direct and indirect effects on entrepreneurial intention (INT) in Thailand....
Low-carbon technology innovations (LTI) have attracted wide attention due to their effects on reducing carbon emissions, and improving LTI behavior is a kind of beneficial measures on carbon emissions control in enterprise. The current research explo...
Recent methods of extracting relational triples mainly focus on the overlapping problem and achieve considerable performance. Most previous approaches extract triples solely conditioned on context words, but ignore the potential relations among the e...
Entity and relation extraction (ERE) is a core task in information extraction. This task has always faced the overlap problem. It was found that heterogeneous graph attention networks could enhance semantic analysis and fusion between entities and re...
Geographic relation completion contributes greatly to improving the quality of large-scale geographic knowledge graphs (GeoKGs). However, the internal features of a GeoKG used in large-scale GeoKGs embedding are often limited by the weak connectivity...
Knowledge Graph Reasoning (KGR) aims to deduce missing or novel knowledge by learning structured information and semantic relationships within Knowledge Graphs (KGs). Despite significant advances achieved by deep neural networks in recent years, exis...
Joint named entity recognition and relation extraction is an essential natural language processing task that aims to identify entities and extract the corresponding relations in an end-to-end manner. At present, compared with the named entity recogni...
In multiple related time series prediction problems, the key is capturing the comprehensive influence of the temporal dependencies within each time series and the interactional dependencies between time series. At present, most time series prediction...
Electronic medical records (EMRs) contain a variety of valuable medical entities and their relations. The extraction of medical entities and their relations has important application value in the structuring of EMR and the development of various type...
In this study, we examine the relation between social network site (SNS) usage and the personal networks of immigrants, using a unique dataset composed of a representative sample of immigrants living in the Netherlands. In theory, SNSs can be a helpf...
Extracting entities and relations, as a crucial part of many tasks in natural language processing, transforms the unstructured text information into structured information and provides corresponding data support for knowledge graph (KG) and knowledge...
To investigate the human-related factors associated with suffocation on ships during docking repair, a comprehensive analysis model composed of a Bayesian network (BN) and a complex network (CN) is proposed in the present study. The principle of even...
Buildings are important entity objects of cities, and the classification of building shapes plays an indispensable role in the cognition and planning of the urban structure. In recent years, some deep learning methods have been proposed for recognizi...
Supervised learning methods excel in traditional relation extraction tasks. However, the quality and scale of the training data heavily influence their performance. Few-shot relation extraction is gradually becoming a research hotspot whose objective...
Multi-Entity Bayesian Network (MEBN) is a knowledge representation formalism combining Bayesian Networks (BNs) with First-Order Logic (FOL). MEBN has sufficient expressive power for general-purpose knowledge representation and reasoning, and is the l...
Few-shot relation extraction constitutes a critical task in natural language processing. Its aim is to train a model using a limited number of labeled samples when labeled data are scarce, thereby enabling the model to rapidly learn and accurately id...
The main purpose of the joint entity and relation extraction is to extract entities from unstructured texts and extract the relation between labeled entities at the same time. At present, most existing joint entity and relation extraction networks ig...
Using three spatial network measures of “space syntax”, this correlational study describes four interaction-related behaviors among three groups of users in relation to visibility and accessibility of spaces in four adult intensive care units (ICUs)...
The transfer of electricity-related water across regions and sectors provides an opportunity to alleviate water stress and make the development of the power system sustainable. Yet, the key node identification and properties of the electricity-relate...
As energy conversion systems continue to grow in complexity, pneumatic control valves may exhibit unexpected anomalies or trigger system shutdowns, leading to a decrease in system reliability. Consequently, the analysis of time-domain signals and the...
In recent advancements within natural language processing (NLP), lexical networks play a crucial role in representing semantic relationships between words, enhancing applications from word sense disambiguation to educational tools. Traditional method...
Fertilizer carbon emissions contribute the largest proportion to agricultural carbon emissions in China, while the extension of low-carbon fertilization technologies (LCFTs) is an effective measure to address this issue. Research suggests that the re...
In industrial vision, the lack of defect samples is one of the key constraints of depth vision quality inspection. This paper mainly studies defect detection under a small training set, trying to reduce the dependence of the model on defect samples b...
The structure of the network among firms participating in global value chains is an important factor in understanding the changes in China’s carbon emissions. This paper focuses on the interdependence between firms and the interconnected networ...
The task to extract relations tries to identify relationships between two named entities in a sentence. Because a sentence usually contains several named entities, capturing structural information of a sentence is important to support this task. Curr...
Distantly Supervised relation extraction methods can automatically extract the relation between entity pairs, which are essential for the construction of a knowledge graph. However, the automatically constructed datasets comprise amounts of low-quali...
Deep learning has achieved great success in hyperspectral image classification. However, when processing new hyperspectral images, the existing deep learning models must be retrained from scratch with sufficient samples, which is inefficient and unde...
Deep network models rely on sufficient training samples to perform reasonably well, which has inevitably constrained their application in classification of hyperspectral images (HSIs) due to the limited availability of labeled data. To tackle this pa...
Many existing fault diagnosis methods based on deep learning (DL) require numerous fault samples to train the diagnosis model. However, in industrial applications, rotating machines (RMs) operate in normal states for most of their service life with f...
Utilizing relational networking and cultural assets provide an arena for village development associations (VDAs) to fill the gaps in infrastructure in resource-limited communities of Cameroon’s north-west region. This case study interrogates th...
Few-shot learning refers to training a model with a few labeled data to effectively recognize unseen categories. Recently, numerous approaches have been suggested to improve the extraction of abundant feature information at hierarchical layers or mul...
Relation extraction is one of the most important intelligent information extraction technologies, which can be used to construct and optimize services in intelligent communication systems (ICS). One issue with the existing relation extraction approac...
Deep learning and convolutional neural networks (CNN) have been widely applied in polarimetric synthetic aperture radar (PolSAR) image classification, and satisfactory results have been obtained. However, there is one crucial issue that still has not...
Biomedical texts are relatively obscure in describing relations between specialized entities, and the automatic extraction of drug–drug or drug–disease relations from massive biomedical texts presents a challenge faced by many researchers...
The current study was conducted to examine the consumption process of tourists through the SIPS model as they experienced tourism-related information and products on social networking sites. Data was collected online from Koreans who have experience...
For information security, entity and relation extraction can be applied in sensitive information protection, data leakage detection, and other aspects. The current approaches to entity relation extraction not only ignore the relevance and dependency...
In production, due to natural conditions or process peculiarities, a single product often may exhibit more than one type of defect. The accurate identification of all defects has an important guiding significance and practical value to improve the pl...
11 January 2020
Escalator-related injuries have become an important issue in daily metro operation. To reduce the probability and severity of escalator-related injuries, this study conducted a probability and severity analysis of escalator-related injuries by using...
Recent years have seen a rise in interest in document-level relation extraction, which is defined as extracting all relations between entities in multiple sentences of a document. Typically, there are multiple mentions corresponding to a single entit...
Few-shot relation extraction (FSRE) constitutes a critical task in natural language processing (NLP), involving learning relationship characteristics from limited instances to enable the accurate classification of new relations. The existing research...
Background: Age-related macular degeneration (AMD) is a multifactorial disease encompassing a complex interaction between aging, environmental risk factors, and genetic susceptibility. The study aimed to determine whether there is a relationship betw...
The urban environment near the road infrastructure is particularly affected by traffic emissions. This problem is exacerbated at road junctions. The roadside concentration of particulate (PM2.5 and PM10) emissions depends on traffic parameters, meteo...
Distantly supervised relation extraction is the most popular technique for identifying semantic relation between two entities. Most prior models only focus on the supervision information present in training sentences. In addition to training sentence...
Common video-based object detectors exploit temporal contextual information to improve the performance of object detection. However, detecting objects under challenging conditions has not been thoroughly studied yet. In this paper, we focus on improv...
Although hyperspectral data provide rich feature information and are widely used in other fields, the data are still scarce. Training small sample data classification is still a major challenge for HSI classification based on deep learning. Recently,...
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