scholarly journals A Survey on Aspect-Based Sentiment Classification

2021 ◽  
Author(s):  
Gianni Brauwers ◽  
Flavius Frasincar
Keyword(s):  
State Of The Art ◽  
Knowledge Bases ◽  
Sentiment Classification ◽  
Automatic Extraction ◽  
Learning Models ◽  
Text Documents ◽  
Fine Grained ◽  
Knowledge Based ◽  
Transformer Model ◽  
The Web

With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Furthermore, trends in the research on ABSC are identified and a discussion is provided on the ways in which the field of ABSC can be advanced in the future.

scholarly journals Sentiment Classification Using Convolutional Neural Networks

2019 ◽  
Vol 9 (11) ◽  
pp. 2347 ◽  
Author(s):  
Hannah Kim ◽  
Young-Seob Jeong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Text Classification ◽  
State Of The Art ◽  
Sentiment Classification ◽  
Learning Models ◽  
Text Data ◽  
Textual Data ◽  
Better Than

As the number of textual data is exponentially increasing, it becomes more important to develop models to analyze the text data automatically. The texts may contain various labels such as gender, age, country, sentiment, and so forth. Using such labels may bring benefits to some industrial fields, so many studies of text classification have appeared. Recently, the Convolutional Neural Network (CNN) has been adopted for the task of text classification and has shown quite successful results. In this paper, we propose convolutional neural networks for the task of sentiment classification. Through experiments with three well-known datasets, we show that employing consecutive convolutional layers is effective for relatively longer texts, and our networks are better than other state-of-the-art deep learning models.

scholarly journals OpenKG Chain: A Blockchain Infrastructure for Open Knowledge Graphs

2021 ◽  
pp. 1-18
Author(s):  
Huajun Chen ◽  
Ning Hu ◽  
Guilin Qi ◽  
Haofen Wang ◽  
Zhen Bi ◽  
...  
Keyword(s):  
Open Data ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
Cumulative Number ◽  
Trust Network ◽  
Fine Grained ◽  
Knowledge Based ◽  
Granularity Level ◽  
Knowledge Graphs ◽  
Structured Graphs

Abstract The early concept of knowledge graph originates from the idea of the Semantic Web, which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things. Currently publishing knowledge bases as open data on the Web has gained significant attention. In China, CIPS(Chinese Information Processing Society) launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge Graphs. Unlike existing open knowledge-based programs, OpenKG chain is envisioned as a blockchain-based open knowledge infrastructure. This article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain, a blockchain-based trust network. We have completed the test of the underlying blockchain platform, as well as the on-chain test of OpenKG's dataset and toolset sharing as well as fine-grained knowledge crowdsourcing at the triple level. We have also proposed novel definitions: K-Point and OpenKG Token, which can be considered as a measurement of knowledge value and user value. 1033 knowledge contributors have been involved in two months of testing on the blockchain, and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,000. For the first time, We have tested and realized on-chain sharing of knowledge at entity/triple granularity level. At present, all operations on the datasets and toolset in OpenKG.CN, as well as the triplets in OpenBase, are recorded on the chain, and corresponding value will also be generated and assigned in a trusted mode. Via this effort, OpenKG chain looks to provide a more credible and traceable knowledge-sharing platform for the knowledge graph community.

scholarly journals Fine-grained Image Classification by Visual-Semantic Embedding

2018 ◽  
Author(s):  
Huapeng Xu ◽  
Guilin Qi ◽  
Jingjing Li ◽  
Meng Wang ◽  
Kang Xu ◽  
...  
Keyword(s):  
Image Classification ◽  
Prior Knowledge ◽  
Visual Information ◽  
State Of The Art ◽  
Knowledge Bases ◽  
Image Features ◽  
Challenging Problem ◽  
Fine Grained ◽  
Unstructured Text ◽  
Conventional Computer

This paper investigates a challenging problem,which is known as fine-grained image classification(FGIC). Different from conventional computer visionproblems, FGIC suffers from the large intraclassdiversities and subtle inter-class differences.Existing FGIC approaches are limited to exploreonly the visual information embedded in the images.In this paper, we present a novel approachwhich can use handy prior knowledge from eitherstructured knowledge bases or unstructured text tofacilitate FGIC. Specifically, we propose a visual-semanticembedding model which explores semanticembedding from knowledge bases and text, andfurther trains a novel end-to-end CNN frameworkto linearly map image features to a rich semanticembedding space. Experimental results on a challenginglarge-scale UCSD Bird-200-2011 datasetverify that our approach outperforms several state-of-the-art methods with significant advances.

DSC_NEXT: Document-Level Sentiment ClassificatioN with EXTensional-Information

2021 ◽  
pp. 2150001
Author(s):  
Pengyuan Liu ◽  
Chenghao Zhu ◽  
Yi Wu
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Network Models ◽  
User Preferences ◽  
Sentiment Classification ◽  
Learning Models ◽  
Neural Network Models ◽  
Product Features ◽  
Current State ◽  
Document Level

Document-level sentiment classification is to assign an overall sentiment polarity to an opinion document. Some researchers have already realized that, in addition to document texts, extensional-information such as product features and user preferences can be quite useful. Many previous studies represent them as ID-type extensional-information and incorporate them into deep learning models. However, they ignore the descriptive extensional information that is also useful for document representations. This paper covers the following aspects: (1) introduces the Description of Opinion Target (DOT), a new extensional-information for document-level sentiment classification, (2) builds the Document-level Sentiment ClassificatioN with EXTensional-information (DSC_NEXT) dataset which consists of three datasets: IMDB_NEXT, Yelp_NEXT and CMRDB_NEXT and (3) validates the effectiveness of DOT by performing experiments based on current state-of-the-art (SOTA) document-level sentiment analysis methods. Implications for using extensional-information in neural network models are also considered.

scholarly journals Knowledge Engineering the Web

2021 ◽  
Vol 11 (1) ◽  
pp. 68-76
Author(s):  
Ben Choi ◽  
Keyword(s):  
Search Engine ◽  
Knowledge Engineering ◽  
State Of The Art ◽  
The State ◽  
Human Knowledge ◽  
Search Results ◽  
New Methods ◽  
Knowledge Based ◽  
The Web

This paper focuses on the largest source of human knowledge: The Web. It presents the state of the art and patented technologies on search engine, automatic organization of webpages, and knowledge-based automatic webpage summarization. For the patented search engine technology, it describes new methods to present search results to the users and through browsers to allow the users to customize and organize webpages. For the patented classification technology, it describes new methods to automatically organize webpages into categories. For the knowledge-based summarization technology, it presents new technics for computers to "read" webpages and then to "write" a summary by creating new sentences to describe the contents of the webpages. These search engine, classification, and summarization technologies build a strong framework for knowledge engineering the Web.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

Cyberphysical Design Automation Framework for Knowledge-based Engineering

2013 ◽  
Vol 1 (1) ◽  
pp. 158-178
Author(s):  
Urcun John Tanik
Keyword(s):  
System Design ◽  
Design Process ◽  
Design Automation ◽  
Design Theory ◽  
Knowledge Bases ◽  
Knowledge Based ◽  
Knowledge Based Engineering ◽  
Cyberphysical System ◽  
Automation Framework

Cyberphysical system design automation utilizing knowledge based engineering techniques with globally networked knowledge bases can tremendously improve the design process for emerging systems. Our goal is to develop a comprehensive architectural framework to improve the design process for cyberphysical systems (CPS) and implement a case study with Axiomatic Design Solutions Inc. to develop next generation toolsets utilizing knowledge-based engineering (KBE) systems adapted to multiple domains in the field of CPS design automation. The Cyberphysical System Design Automation Framework (CPSDAF) will be based on advances in CPS design theory based on current research and knowledge collected from global sources automatically via Semantic Web Services. A case study utilizing STEM students is discussed.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Survey of the State-of-the-Art Expert/Knowledge Based Systems in Civil Engineering.

1986 ◽  
Author(s):  
Simon S. Kim ◽  
Mary Lou Maher ◽  
Raymond E. Levitt ◽  
Martin F. Rooney ◽  
Thomas J. Siller
Keyword(s):  
Civil Engineering ◽  
Expert Knowledge ◽  
State Of The Art ◽  
The State ◽  
Knowledge Based Systems ◽  
Knowledge Based
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