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Journal
Yu Yuqing;Wang Yuzhu;Mu Jingqin;Li Wei;Jiao Shoutao;Wang Zhenhua;Lv Pengfei;Zhu Yueqin;
Expert Systems With ApplicationsVolume 206, Issue , 2022, PP
摘要: Mineral named entity recognition (MNER) is the extraction for the specific types of entities from unstructured Chinese mineral text, which is a prerequisite for building a mineral knowledge graph. MNER can also provide important data support for the work related to mineral resources. Chinese mineral text has many types of entities, complex semantics, and a large number of rare characters. To extract entities from Chinese mineral literature, this paper proposes an MNER model based on deep learnin关键词: Named entity recognition;Mineral text;BERT;CRF
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Journal
摘要: Aspect-based sentiment analysis (ABSA) aims at determining the sentiment polarity of the given aspect term in a sentence. Recently, graph convolution network (GCN) has been used in the ABSA task and obtained promising results. Despite the proliferation of the methods and their success, prevailing models based on GCN lack a powerful constraint mechanism for the message passing to aspect terms, introducing heavy noise during graph convolution. Further, they simply average the subword vectors from 关键词: Aspect-based sentiment analysis;Graph convolutional network;Multi-head self attention;BERT
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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: Several factors hinder information retrieval in the medical profession. Consumers (layman people) often struggle to learn medical terms. Because medical terms are more evident to professionals, it is difficult for consumers to construct a query using medical terms. Consumers would find it easier to access relevant medical information if medical words relevant to their query were automatically added. Various kiosks use approaches using machine vision to form the user queries and monitor their hea关键词: BERT;Fuzzy logic;Pseudo relevance feedback;Query expansion;Term selection;Machine learning
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Journal
摘要: Aspect level sentiment analysis is a fine-grained task in sentiment analysis. It extracts aspects and their corresponding sentiment polarity from opinionated text. The first subtask of identifying the opinionated aspects is called aspect extraction, which is the focus of the work. Social media platforms are an enormous resource of unlabeled data. However, data annotation for fine-grained tasks is quite expensive and laborious. Hence unsupervised models would be highly appreciated. The proposed m关键词: Sentiment analysis;Aspect term extraction;Guided LDA;BERT;Semantic similarity
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Journal
Aldahdooh Jehad;Vähä Koskela Markus;Tang Jing;Tanoli Ziaurrehman;
BMC BioinformaticsVolume 23, Issue 1, 2022, PP 245-245
摘要: Background(#br)Drug-target interactions (DTIs) are critical for drug repurposing and elucidation of drug mechanisms, and are manually curated by large databases, such as ChEMBL, BindingDB, DrugBank and DrugTargetCommons. However, the number of curated articles likely constitutes only a fraction of all the articles that contain experimentally determined DTIs. Finding such articles and extracting the experimental information is a challenging task, and there is a pressing need for systematic approa关键词: BERT;Bidirectional encoder representations from transformers;BERT for biomedical data;Drug target interaction prediction;Mining drug target interactions;Biomedical text mining;Bioactivity data;Drug repurposing
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Journal
摘要: The construction of smart courts promotes the in-deep integration of internet, big data, cloud computing and artificial intelligence with judicial trial work, which can both improve trials and ensure judicial justice with more efficiency. High-quality structured legal facts, obtained by extracting information from unstructured legal texts, are the foundation for the construction of smart courts. Based on the strong normative characteristics of Chinese legal text content and structure composition关键词: information extraction;ontology;BERT;Bi-LSTM;CRF;Chinese legal texts
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Journal
摘要: User-generated content based on customer opinions and experience has become a rich source of valuable information for enterprises. The purpose of aspect-based sentiment analysis is to predict the sentiment polarity of specific targets from user-generated content. This study proposes a component focusing multi-head co-attention network model which contains three modules: extended context, component focusing, and multi-headed co-attention, designed to improve upon problems encountered in the past.关键词: Deep learning;Neural network;Sentiment analysis;BERT
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Journal
摘要: Viruses are ubiquitous in humans and various environments and continually mutate themselves. Identifying viruses in an environment without cultivation is challenging; however, promoting the screening of novel viruses and expanding the knowledge of viral space is essential. Homology-based methods that identify viruses using known viral genomes rely on sequence alignments, making it difficult to capture remote homologs of the known viruses. To accurately capture viral signals from metagenomic samp关键词: BERT model;deep learning;metagenome;viral genome;virus classification
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Journal
摘要: Interdisciplinary research promotes the emergence of scientific innovation. Researchers want to find interdisciplinary research in their research field. However, the number of scientific papers published today is increasing, and completing this task by hand is time-consuming and laborious. A neural network is a machine learning model that simulates the connection mode of neurons in the human brain. It is an important application of bionics in the artificial intelligence field. This paper propose关键词: neural network;interdisciplinary research;BERT;deep learning;vector space
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Journal
摘要: BACKGROUND(#br) Biomedical sciences, with their focus on human health and disease, have attracted unprecedented attention in the 21st century. The proliferation of biomedical sciences has also led to a large number of scientific articles being produced, which makes it difficult for biomedical researchers to find relevant articles and hinders the dissemination of valuable discoveries. To bridge this gap, the research community has initiated the article recommendation task, with the aim of recomme关键词: BERT;biomedical article recommendation;methodological comparison;model evaluation;modeling strategy;text representation
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Book Chapter
Singh Vedaant;Tibrewal Vedant;Verma Chetna;Singh Yash Raj;Sinha Twinkle;Shrivastava Vimal K.;
摘要: In the past few decades, the growth of data on the Internet has increased significantly, and even today, tons of data get generated with each passing day. The World Wide Web has become a great source of e-learning, sharing ideas, and interchanging school of thoughts and views. Internet community sites like Twitter, Facebook, and Instagram have gained immense attraction and have gathered a huge pool of daily active users over the past few decades, as they provide a medium to exchange or express t关键词: Twitter;Sentiment analysis;Coronavirus;COVID-19;Classification;Machine learning;NLP;BERT
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Journal
摘要: During the operation of nuclear power system, the instrument detection data often deviates from the normal operating state for a short time due to system or environmental fluctuations. And the control system will send an alarm signal, resulting in the false alarm. Aiming at the problem of false alarm, three algorithms are improved and combined to form a false alarm algorithm in this paper. The algorithm consists of the transient operating parameters processing algorithm and the nuclear power sys关键词: Nuclear power system;False alarm detection;Machine learning;BERT;Sparse auto encoder;Isolation forest
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Journal
Colasanto Francesco;Grilli Luca;Santoro Domenico;Villani Giovanni;
Information SciencesVolume 597, Issue , 2022, PP 341-357
摘要: BERT (Bidirectional Encoder Representations from Transformers) is one of the most popular models in Natural Language Processing (NLP) for Sentiment Analysis. The main goal is to classify sentences (or entire texts) and to obtain a score in relation to their polarity: positive, negative or neutral. Recently, a Transformer-based architecture, the fine-tuned AlBERTo (Polignano et al. (2019)), has been introduced to determine a sentiment score in the financial sector through a specialized corpus of 关键词: BERT;MCMC;Sentiment analysis;Stock market;Price forecasting
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Journal
Tian Jing;Slamu Wushour;Xu Miaomiao;Xu Chunbo;Wang Xue;
SymmetryVolume 14, Issue 5, 2022, PP 1072-1072
摘要: Sentiment analysis is the processing of textual data and giving positive or negative opinions to sentences. In the ABSA dataset, most sentences contain one aspect of sentiment polarity, or sentences of one aspect have multiple identical sentiment polarities, which weakens the sentiment polarity of the ABSA dataset. Therefore, this paper uses the SemEval 14 Restaurant Review dataset, in which each document is symmetrically divided into individual sentences, and two versions of the datasets ATSA a关键词: aspect-based sentiment analysis;SemEval 14 Restaurant Review dataset;BERT;capsule network
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Book Chapter
摘要: Text summarization produces a shortened or condensed version of input text highlighting its central ideas. Generating text summarization manually takes time and effort. This paper investigates several text summarization models based on neural networks, including extractive summarization, abstractive summarization, and abstractive summarization based on the re-writer approach and bottom-up approach. We perform experiments on the CTUNLPSum dataset in Vietnamese comprising 95,579 documents collecte关键词: Extractive summarization;Abstractive summarization;Re-writer;Bottom-up approach;LSTM;BiLSTM;BERT;Copy generator;AllenNLP;OpenNMT
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Book Chapter
摘要: Currently, there is a vast amount of archival finding aids in Portuguese archives, however, these documents lack structure (are not annotated) making them hard to process and work with. In this way, we intend to extract and classify entities of interest, like geographical locations, people’s names, dates, etc. For this, we will use an architecture that has been revolutionizing several NLP tasks, Transformers, presenting several models in order to achieve high results. It is also intended to un关键词: Named Entity Recognition;Attention mechanism;Transformers;BERT;Data annotation
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Book Chapter
摘要: Unbalanced datasets make it hard for text classifiers to learn well. Having limited information in minority classes makes it difficult to classify the unbalanced texts. In this study, a BERT-based uncased model is developed and fine-tuned to address the unbalanced text classification problem. We use the BERT model with 12 layers and 110 M parameters in order to classify an unbalanced dataset, taking into account both the majority and minority groups. The model is being fine-tuned by varying the 关键词: BERT;Precision;Recall;f-measure
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Journal
摘要: Knowledge recombination has become a hot topic in green innovation adoption (GIA) research in recent years. However, previous studies have mainly discussed the knowledge recombination within a single enterprise but ignored inter-enterprise knowledge recombination (Inter-KR). In this paper, a theoretical framework of Inter-KR is developed, and the three features of Inter-KR are discussed: elements, characteristics, and performances. Multisource heterogeneous data combined with Natural language pr关键词: Green innovation adoption;Knowledge recombination;Social network;Patent analysis;Collaboration network;Inter-KR;GIA;NLP;COD;COD_DI;LDA;BERT
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Journal
摘要: When summarizing an article, humans are habituated to fuse multiple related sentences to make the summary more concise and coherent. But most of the previous work focuses on the grammaticality of the fusion process and neglects the mechanism behind which sentences should be fused together. And there also lacks an effective training method for bridging the modules in the model to approach a global optimization. In this paper we propose FusionSum, a novel framework that imitates the behaviors of h关键词: Text summarization;Reinforcement learning;Sentence fusion;BERT
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Journal
摘要: Sentence Ordering refers to the task of rearranging a set of sentences into the appropriate coherent order. For this task, most previous approaches have explored global context-based end-to-end methods using Sequence Generation techniques. In this paper, we put forward a set of robust local and global context-based pairwise ordering strategies, leveraging which our prediction strategies outperform all previous works in this domain. Our proposed encoding method utilizes the paragraph’s rich globa关键词: BERT;Discourse modeling;Natural language processing;Sentence ordering;Transformer architecture
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Journal
摘要: Precision medicine can be defined as the comparison of a new patient with existing patients that have similar characteristics and can be referred to as patient similarity. Several deep learning models have been used to build and apply patient similarity networks (PSNs). However, the challenges related to data heterogeneity and dimensionality make it difficult to use a single model to reduce data dimensionality and capture the features of diverse data types. In this paper, we propose a multi-mode关键词: patient;patient similarity network;precision medicine;big data;personalized healthcare;patient-centered framework;deep learning;electronic health records;transformers;BERT;autoencoder
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Journal
摘要: The online Question and Answering (Q&A) community has grown globally, allowing users to ask, discuss, and answer questions based on shared interests. As a gathering place for people’s knowledge production, collaboration, and dissemination in the current Internet scene, the online Q&A community can intuitively reflect the public’s information needs and behavior. It also collects many sports-related data and becomes an effective vehicle for comprehending mass sports information needs and dissemina关键词: information needs;sports communication;sports in China;online Q&A community;BERT model;topic mining
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Journal
摘要: During natural disasters, social media can provide real time or rapid disaster, perception information to help government managers carry out disaster response efforts efficiently. Therefore, it is of great significance to mine social media information accurately. In contrast to previous studies, this study proposes a multimodal data classification model for mining social media information. Using the model, the study employs Late Dirichlet Allocation (LDA) to identify subject information from mul关键词: multimodal data;LDA;Bert;VGG-16
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Journal
Altamirano Matías;Uribe Pablo;Schlotterbeck Danner;Jiménez Abelino;Araya Roberto;van der Molen Moris Johan;Caballero Daniela;
NeurocomputingVolume 484, Issue , 2022, PP 211-222
摘要: Classroom observation is an essential component to improve the quality of teaching and develop educational research. Nonetheless, traditional observation procedures involve previously trained observers, rendering them expensive and time-consuming. Thus, there is a need for a tool which enables to analyze multiple lessons in short time, represent those lessons consistently, and describe them according to the teaching strategies used over time to give teachers timely and continuous feedback. In th关键词: Time series clustering;Topic modeling;Latent Dirichlet allocation;BERT;Teacher’s talk automatic analysis
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Journal
摘要: Micro-blogging social site Twitter has emerged as a rich source of unstructured text information which could be processed and analysed to extract people’s opinions about several topics and events including natural hazards, energy, sports, transportation, elections etc. The present research study adds a novel perspective in this dimension by extracting citizens’ opinions on several electricity-related issues. Recent research studies in this domain have employed Bag-of-Words (BoW) model for the nu关键词: Deep learning;Twitter;BERT;Complaints classification;Sentiment analysis;Energy
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Journal
摘要: The existing electrocardiogram (ECG) biometrics do not perform well when ECG changes after the enrollment phase because the feature extraction is not able to relate ECG collected during enrollment and ECG collected during classification. In this research, we propose the sequence pair feature extractor, inspired by Bidirectional Encoder Representations from Transformers (BERT)’s sentence pair task, to obtain a dynamic representation of a pair of ECGs. We also propose using the self-attention mech关键词: transformer;BERT;ECG biometrics;self-attention mechanism;deep learning;multi-class classification;convolutional neural network;feature extraction;blind segmentation;artificial neural network
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Journal
摘要: Recent advances have witnessed a growth of herbalism studies adopting a modern scientific approach in molecular medicine, offering valuable domain knowledge that can potentially boost the development of herbalism with evidence-supported efficacy and safety. However, these domain-specific scientific findings have not been systematically organized, affecting the efficiency of knowledge discovery and usage. Existing knowledge graphs in herbalism mainly focus on diagnosis and treatment with an absen关键词: biobert;knowledge graph;herb;chemical;disease;gene;BERT;ontology
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Journal
摘要: Garlic-related misinformation is prevalent whenever a virus outbreak occurs. With the outbreak of COVID-19, garlic-related misinformation is spreading through social media, including Twitter. Bidirectional Encoder Representations from Transformers (BERT) can be used to classify misinformation from a vast number of tweets. This study aimed to apply the BERT model for classifying misinformation on garlic and COVID-19 on Twitter, using 5929 original tweets mentioning garlic and COVID-19 (4151 for f关键词: bidirectional encoder representations from transformers (BERT);COVID-19;garlic;misinformation;Twitter
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Book Chapter
摘要: Making human–computer interaction more organic and personalized for users essentially demands advancement in human emotion recognition. Emotions are perceived by humans considering multiple factors such as facial expressions, voice tonality, and information context. Although significant research has been conducted in the area of unimodal/multimodal emotion recognition in videos using acoustic/visual features, few papers have explored the potential of textual information obtained from the video u关键词: Multimodal;Emotion;Recognition;CMU-MOSEI;Multi-head attention;BERT;Context-aware
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