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Journal
摘要: Relation classification between entities is a fundamental problem in knowledge extraction. It aims at determining if a semantic relation holds between a pair of entities based on textual descriptions. In general, the training data for each relation is limited. Distant supervision has thus been widely used to generate abundant weakly labeled data for the task. These data are noisy, containing many errors. A crucial problem is to select reliable instances for training or weigh them adequately. How关键词: Relation classification;Meta-learning;Instance weighting;Noisy label;Reference data
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Journal
摘要: Relation classification (RC) is a fundamental task to building knowledge graphs and describing semantic formalization. It aims to classify a relation between the head and the tail entities in a sentence. The existing RC method mainly adopts the distant supervision (DS) scheme. However, DS still has the problem of long-tail and suffers from data sparsity. Recently, few-shot learning (FSL) has attracted people’s attention. It solves the long-tail problem by learning from few-shot samples. The prot关键词: Relation classification;Few-shot learning;Hybrid attention;Loss;BERT
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Journal
摘要: Relation classification (RC) is an essential task in natural language processing (NLP), which extracts relationships of entity pairs in sentences of text. In the paper, a novel target attention convolutional neural network (TACNN) is proposed for the RC by fully utilizing word embedding information and position embedding information. Simultaneously, a target attention mechanism (TAM) is applied into a context layer of the convolutional neural network (CNN) model, which increases the effect of th关键词: Relation classification;Target attention mechanism;Convolutional neural network;Relation matrix
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Book Chapter
摘要: Drug discovery is time consuming and expensive that involves many scientists synthesizing thousands of molecules and running countless experiments over many years. To select viable candidates entering clinical development, in silico 关键词: Absorption, distribution, metabolism and elimination (ADME);drug transporters;Drug–drug interactions (DDI);drug clearance;drug discovery and development;Extended clearance classification system (ECCS);hepatobiliary elimination;in silico;pharmacokinetics;physiochemical properties;renal elimination;structure–activity relationship (SAR);transporter‐mediated drug clearance
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Book Chapter
摘要: How are lonely individuals related to brands? Are they more loyal to brands? Concerning this research question, prior studies showed inconsistent findings—some suggests stronger loyalty of lonely individuals while others suggests weaker loyalty and more switching behavior of lonely individuals.The present research proposes that loneliness can sometimes make consumers feel more loyal toward brands and other times appear less loyal, and the difference depends on the dimensions of loneliness. Altho关键词: Brand loyalty;Brand assortment;Loneliness;Social relationship
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Journal
摘要: Meaningful Information extraction is an extremely important and challenging task due to the ever growing size of data. Training and evaluating automated systems for the task requires annotated datasets which are rarely available because of the great amount of human effort and time required for annotating data. The dataset described in this manuscript, CustFRE, is meant for systems that learn extracting family relations from text. Sentences having at least two persons have been collected from the关键词: Natural language processing;Relation classification;Machine learning;Family relations
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Journal
Deng Sinuo;Shi Ge;Feng Chong;Wang Yashen;Liao Lejian;
Applied SciencesVolume 12, Issue 7, 2022, PP 3437-3437
摘要: Relation classification tends to struggle when training data are limited or when it needs to adapt to unseen categories. In such challenging scenarios, recent approaches employ the metric-learning framework to measure similarities between query and support examples and to determine relation labels of the query sentences based on the similarities. However, these approaches treat each support class independently from one another, never looking at the entire task as a whole. Because of this, they a关键词: relation classification;few-shot learning;task embedding
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Book Chapter
摘要: In view of the large amount of information in domestic policy texts and the rich semantic information, it is difficult for researchers to quickly and effectively sort out and compare and analyze the content when reading. This paper takes the policy text data resources in the energy field as an example, and sorts them out according to the types of resources. On the basis of sorting out the policy resources, it combines the characteristics of policy text information with deep learning technology, 关键词: Deep learning;Policy text;Neural network;Entity recognition;Relationship classification
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Journal
摘要: Relation classification is an important semantic processing task in the field of natural language processing. The deep learning technology, which combines Convolutional Neural Network and Recurrent Neural Network with attention mechanism, has always been the mainstream and state-of-art method. The LSTM model based on recurrent neural network dynamically controls the weight by gating, which can better extract the context state information in time series and effectively solve the long-standing pro关键词: Relation classification;attention mechanism;BERT;LSTM
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Journal
Review
摘要: The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers. Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making, future scenario generation关键词: cause-effect relation;causality survey;causality mining;deep learning;causality extraction;relation classification
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Journal
摘要: Drug-Drug Interaction (DDI) extraction is the task of identifying drug entities and the potential interactions between drug pairs from biomedical literature. Computer-aided extraction of DDIs is vital for drug discovery, as this process remains extremely expensive and time consuming. Therefore, Machine Learning-based approaches can reduce the laborious task during the drug development cycle. Numerous traditional and Neural Network-based approaches for Drug Named Entity Recognition (DNER) and the关键词: Drug-drug interaction;Relationship extraction;Drug named entity recognition;Relation classification;Pipeline
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Journal
摘要: Lung cancer is the leading cause of cancer deaths worldwide. Clinical staging of lung cancer plays a crucial role in making treatment decisions and evaluating prognosis. However, in clinical practice, approximately one-half of the clinical stages of lung cancer patients are inconsistent with their pathological stages. As one of the most important diagnostic modalities for staging, chest computed tomography (CT) provides a wealth of information about cancer staging, but the free-text nature of th关键词: clinical staging;information extraction;lung cancer;named entity recognition;relation classification
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Journal
摘要: Accurate detection of bleeding events from electronic health records (EHRs) is crucial for identifying and characterizing different common and serious medical problems. To extract such information from EHRs, it is essential to identify the relations between bleeding events and related clinical entities (eg, bleeding anatomic sites and lab tests). With the advent of natural language processing (NLP) and deep learning (DL)-based techniques, many studies have focused on their applicability for vari关键词: BERT;CNN;GCN;bleeding;electronic health records;relation classification
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Journal
Research Article
Long Jun;Wang Ye;Wei Xiangxiang;Ding Zhen;Qi Qianqian;Xie Fang;Qian Zheman;Huang Wenti;
Applied SciencesVolume 11, Issue 4, 2021, PP 1377-1377
摘要: Relation classification is an important task in the field of natural language processing, and it is one of the important steps in constructing a knowledge graph, which can greatly reduce the cost of constructing a knowledge graph. The Graph Convolutional Network (GCN) is an effective model for accurate relation classification, which models the dependency tree of textual instances to extract the semantic features of relation mentions. Previous GCN based methods treat each node equally. However, t关键词: graph convolutional network;relation classification;natural language processing
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Book Chapter
摘要: Genetics deals with traits of inheritance from parents to offspring, and is an important source of information for taxonomy and phylogenetics. This chapter provides an overview of macro‐ and microevolutionary relationships of radiolaria based on molecular phylogeny. The rhizarian group has one unique molecular character vs. all other eukaryotes: insertions between ubiquitin monomers. The studies summarized in this chapter clearly indicate the enormous potential to improve radiolarian taxonomy, a关键词: eukaryotes;family‐level phylogeny;genetics;microevolutionary relationships;phylogenetics;pico‐radiolarian material;radiolarian taxonomy
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Journal
摘要: Several recent efforts have been devoted to enhancing pre-trained language models (PLMs) by utilizing extra heterogeneous knowledge in knowledge graphs (KGs), and achieved consistent improvements on various knowledge-driven NLP tasks. However, most of these knowledge-enhanced PLMs embed static sub-graphs of KGs (“knowledge context”), regardless of that the knowledge required by PLMs may change dynamically according to specific text (“textual context”). In this paper, we propose a novel framework关键词: Pre-trained language model;Knowledge graph;Entity typing;Relation classification
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Journal
Van-Hien Tran;Van-Thuy Phi;Akihiko Kato;Hiroyuki Shindo;Taro Watanabe;Yuji Matsumoto;
自然言語処理Volume 28, Issue 4, 2021, PP 965-994
摘要: 抄録(#br)The joint entity and relation extraction task detects entity pairs along with their relations to extract relational triplets. A recent study (Yu et al. 2020) proposed a novel decomposition strategy that splits the task into two interrelated subtasks: detection of the head-entity (HE) and identification of the corresponding tail-entity and relation (TER) for each extracted head-entity. However, this strategy suffers from two major problems. First, if the HE detection task fails to find a v关键词: Information Extraction;Joint Extraction;Named-Entity Recognition;Relation Classification;Decomposition Strategy
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Book Chapter
Darling D. Christopher; Ghahari Hassan; Gibson Gary A. P.;
Chalcidoidea of Iran (Insecta: Hymenoptera)Volume , Issue , 2021, PP -294
摘要: This chapter includes differential characters to distinguish the family Perilampidae (Chalcidoidea), hypothesized phylogenetic relationships with other families, and general biological attributes of the family. Previous cataloguing efforts of the Iranian fauna for the family are summarized, as well as the information included in the checklist of species for the family. This summary information includes the number of species recorded from Iran, any newly recorded species, a comparison of the Iran关键词: geographical distribution;new geographic records;fauna;catalogues;Caucasus Mountains;checklists;mountains;natural enemies;phylogenetics;relationships;biological control agents;taxonomy;hosts;morphology;Azerbaidzhan;catalogs;Union of Soviet Socialist Republics;Western Asia;Jugoslavia;biological control organisms;biocontrol agents;systematics;Perilampidae;Iran;Armenia;Asia;Azerbaijan;Central Europe;Europe;USSR;West Asia;Yugoslavia;Republic of Georgia;Developing Countries;Middle East;Threshold Countries;West Asia;Asia;Developed Countries;Europe;Hymenoptera;insects;Hexapoda;arthropods;invertebrates;animals;eukaryotes;Balkans;Southern Europe;Mediterranean Region
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Book Chapter
Gibson Gary A. P.; Ghahari Hassan; Doğanlar Mikdat;
Chalcidoidea of Iran (Insecta: Hymenoptera)Volume , Issue , 2021, PP -366
摘要: This chapter includes differential characters to distinguish the family Tetracampidae (Chalcidoidea), hypothesized phylogenetic relationships with other families, and general biological attributes of the family. Previous cataloguing efforts of the Iranian fauna for the family are summarized, as well as the information included in the checklist of species for the family. This summary information includes the number of species recorded from Iran, any newly recorded species, a comparison of the Ira关键词: geographical distribution;new geographic records;fauna;catalogues;checklists;natural enemies;phylogenetics;relationships;taxonomy;morphology;hosts;biological control agents;Tetracampidae;catalogs;Union of Soviet Socialist Republics;Jugoslavia;systematics;biological control organisms;biocontrol agents;Hymenoptera;Iran;USSR;Yugoslavia;Developing Countries;Middle East;Threshold Countries;West Asia;Asia;Balkans;Southern Europe;Europe;Mediterranean Region;insects;Hexapoda;arthropods;invertebrates;animals;eukaryotes
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Book Chapter
Zerova Marina D.; Janšta Petr; Ghahari Hassan; Fursov Victor N.; Gibson Gary A. P.; Popescu Irinel E.;
Chalcidoidea of Iran (Insecta: Hymenoptera)Volume , Issue , 2021, PP -384
摘要: This chapter includes differential characters to distinguish the family Torymidae (Chalcidoidea), hypothesized phylogenetic relationships with other families, and general biological attributes of the family. Previous cataloguing efforts of the Iranian fauna for the family are summarized, as well as the information included in the checklist of species for the family. This summary information includes the number of species recorded from Iran, any newly recorded species, a comparison of the Iranian关键词: geographical distribution;new geographic records;fauna;catalogues;Caucasus Mountains;checklists;natural enemies;phylogenetics;relationships;taxonomy;morphology;hosts;biological control agents;Azerbaidzhan;catalogs;Union of Soviet Socialist Republics;Western Asia;Jugoslavia;systematics;biological control organisms;biocontrol agents;Torymidae;Iran;Armenia;Asia;Azerbaijan;Central Europe;Europe;USSR;West Asia;Yugoslavia;Developing Countries;Middle East;Threshold Countries;West Asia;Asia;Developed Countries;Europe;Hymenoptera;insects;Hexapoda;arthropods;invertebrates;animals;eukaryotes;Balkans;Southern Europe;Mediterranean Region
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Book Chapter
Ghahari Hassan; Pintureau Bernard; Viggiani Gennaro; Hayat Mohammad; Fursov Victor N.;
Chalcidoidea of Iran (Insecta: Hymenoptera)Volume , Issue , 2021, PP -398
摘要: This chapter includes differential characters to distinguish the family Trichogrammatidae (Chalcidoidea), hypothesized phylogenetic relationships with other families, and general biological attributes of the family. Previous cataloguing efforts of the Iranian fauna for the family are summarized, as well as the information included in the checklist of species for the family. This summary information includes the number of species recorded from Iran, any newly recorded species, a comparison of the关键词: geographical distribution;new geographic records;fauna;catalogues;Caucasus Mountains;checklists;natural enemies;phylogenetics;relationships;taxonomy;morphology;hosts;biological control agents;parasitoids;parasites;Azerbaidzhan;catalogs;Union of Soviet Socialist Republics;Western Asia;Jugoslavia;systematics;biological control organisms;biocontrol agents;Trichogrammatidae;Iran;Armenia;Asia;Azerbaijan;Central Europe;Europe;USSR;West Asia;Yugoslavia;Developing Countries;Middle East;Threshold Countries;West Asia;Asia;Developed Countries;Europe;Hymenoptera;insects;Hexapoda;arthropods;invertebrates;animals;eukaryotes;Balkans;Southern Europe;Mediterranean Region
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Book Chapter
Tucker Myron R.;Burton Richard G.;Figueroa Aaron D.;Carrao Vincent;Patel Riddhi;Weaver Bryan;Jacob Gregg A.;Ivory Joseph W.;
摘要: This chapter guides the candidate through the workup, analysis, and surgical management of dentofacial deformities. We start with the clinical features of common dentofacial deformities and the corresponding objective and radiographic findings. This aids the candidate in establishing the appropriate skeletal and dental diagnosis. The surgical options to treat the diagnoses are discussed and include management of complications of orthognathic surgery. The nuances of cleft lip and palate are discu关键词: Skeletal facial deformity;Open bite;Transverse discrepancies;Dental compensation;Tooth size discrepancy;Arch alignment;Surgical hooks;Centric relation;Centric occlusion;Model surgery;Virtual surgical planning;Lateral cephalogram;Orthopantogram;Submental vertex;LeFort I osteotomy;BSSO;IVRO;Genioplasty;Scleral show;Negative vector;Facial thirds;Cheek bone–nasal base–lip contour;Facial midline;Cephalometric analysis;Nasolabial angle;Steiner analysis;Wits appraisal;Growth evaluation;Maxillary hyperplasia;Mandibular hypoplasia;Vertical excess;Maxillary hypoplasia;Mandibular hyperplasia;Trigeminocardiac reflex;Condylar sag;Pseudoaneurysm;SARPE;Hunsuck modification;Dal Pont modification;Epker modification;Bad split;Condylar resorption;Hierarchy of stability;Cleft lip and palate;Distraction osteogenesis;OSA;Polysomnography;AHI;Mueller’s maneuver;Fujita classification;REM sleep;STOP BANG;Epworth Sleepiness Scale;BMI;Mallampati score;Nasopharyngoscopy;Posterior airway space;CPAP;Stanford protocol;UPPP;Tracheostomy;Genioglossus advancement;Telegnathic surgery
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Book Chapter
摘要: Classifying data by linking it to a set of labels with a degree of membership is the objective of the multi-labels classification. These labels or classes can have order relationships between them, which can affect the predictive quality of classifiers. Consider these relationships or ignore them, when building the classifier, each has its drawbacks. The first approach facilitates the spread of learning errors and increases complexity of the task, especially if there are cyclical relationships b关键词: Multi-label classification;Cyclical dependencies;Binary classifier;Removing cyclical dependencies;Decision trees
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Journal
Research Article
摘要: Relation classification is one of the most fundamental tasks in the area of cross-media, which is essential for many practical applications such as information extraction, question&answer system, and knowledge base construction. In the cross-media semantic retrieval task, in order to meet the needs of cross-media uniform representation and semantic analysis, it is necessary to analyze the semantic potential relationship and construct semantic-related cross-media knowledge graph. The relationship关键词: Cross-Media Retrieval;Recurrent Neural Networks;Relation classification;Entity direction;Discrimination Information
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Book Chapter
摘要: The purpose of this lesson on working with large and complex datasets is to provide a realistic demonstration of how R is used for challenging analyses, challenging because the dataset is fairly large, challenging because there are many variables requiring attention, challenging because there are missing data, challenging because certain subjects require special accommodation, challenging because selected data in the original dataset need to be put into filtered subsets, etc. Fortunately, R can 关键词: Analysis of variance (ANOVA);Association;Association plot;bagplot or bivariate boxplot;Bar plot or bar chart;Beanplot;Beeswarm plot;Big data;Boolean selection;Boxplot or box-and-whiskers plot;Box-percentile plot;Coefficient of correlation;Color gradient plot;Correlation;Correlogram;Density plot;Dotplot or dotchart;Engelmann–Hecker (EH) plot;Exploratory data analysis (EDA);Gantt chart;Graphical themes;Hexbin plot;Histogram;Interaction plot;International classification of diseases (ICD);Line chart or line graph;Long format data;Mosaic plot;Nonparametric;Normality;Parametric;Pearson’s r;Pie chart;Pirate chart;Probability;Quantile-quantile (Q-Q) plot;Regression;Scatter plot or scatter diagram;Scatterplot matrix (SPLOM);Staircase plot;Stem-and-leaf plot;Stripchart;Sunflower scatterplot;Tidyverse;Trellis graphics;Triangular plot for 3-D representation;Violin plot;Waffle chart or squared pie chart;Wide format data
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Journal
摘要: Abstract(#br)Relation classification is an important task in natural language processing fields. The goal is to predict predefined relations for the marked nominal pairs in given sentences. State-of-the-art works usually focus on using deep neural networks as classifier to conduct the relation prediction. The rich semantic information of relationships in the triples of existing knowledge graph (KG) can be used as additional supervision for relation classification. However, these relationships we关键词: Relation classification; Knowledge graph embedding; Transformer
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Journal
摘要: 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 train a RC model which facilitates to boost the knowledge base. Traditional RC models cannot extract new relations unseen during training since they define RC as a multiclass classification problem. The recent development of few-shot learning (F关键词: distant supervision;few-shot learning;knowledge base;multiple instance learning;relation classification
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Journal
摘要: Relation classification is an important research area in the field of natural language processing (NLP), which aims to recognize the relationship between two tagged entities in a sentence. The noise caused by irrelevant words and the word distance between the tagged entities may affect the relation classification accuracy. In this paper, we present a novel model multi-head attention long short term memory (LSTM) network with filter mechanism (MALNet) to extract the text features and classify the关键词: relation classification;attention mechanism;bidirectional LSTM network;natural language processing
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Book Chapter
摘要: Learning ontological relations is an important step on the way to automatically developing ontologies. This paper introduces a novel way to exploit WordNet [], the combination of pre-trained word embeddings and deep neural networks for the task of ontological relation classification. The data from WordNet and the knowledge encapsulated in the pre-trained word vectors are combined into an enriched dataset. In this dataset a pair of terms that are linked in WordNet through some ontological relati关键词: Semantic relation classification;Word embeddings;Deep learning;Ontologies;WordNet
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Book Chapter
摘要: The English Premier League is the most competitive, televised, and widely watched football tournament. The research work discusses processing the historical match data and historical betting data for predicting the results of the future matches. The data processing consist of four stages, feature extraction, removing missing data, shuffling, and splitting dataset and experimented three models for match outcome and goal difference predictions. The outcome of the matches played between the competi关键词: Classification;Regression;Sequential minimal optimization;AdaBoost;Bayesian network;Correlation coefficient;Correlation-based feature selection;Prediction;English Premier League;Data processing;Feature extraction
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