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Prediction of Question Tags Based on LDA and Deep Neural Network

Ashwin Cherry Mathew
2020 International Journal of Computing Communications and Networking  
This paper proposes a fully automated tagging system which uses Deep Neural Network and Natural Language Processing to generate tags from the derived knowledge unit.  ...  Earlier manual tagging was used to construct question banks. However it is time consuming and leads to many other consistency issues.  ...  Objective The paper deals with text mining, where tags are predicted based on the Convolution Neural Network.  ... 
doi:10.30534/ijccn/2020/02922019 fatcat:3c4gjhcv75c5xdn2oeivfdj364

Statistica Software: A State Of The Art Review

S. Sarumathi, N. Shanthi, S. Vidhya, P. Ranjetha
2015 Zenodo  
The foremost aspire of data mining method is to extract data from a huge data set into several forms that could be comprehended for additional use.  ...  Data mining idea is mounting rapidly in admiration and also in their popularity.  ...  neural networks, self-organizing feature maps, generalized regression neural networks, principle component network, linear models, and cluster networks.  ... 
doi:10.5281/zenodo.1100352 fatcat:pg5z24gt3vffraxzlfeg7fiydy

Provide a data mining algorithm for text classification based on text content emotions using neural network

Ebrahim Haydari, Amir Reza Estakhrian Haghighi
2015 Environment conservation journal  
The results above show that the proposed algorithm is a neural network classification accuracy of 96% negative polarity and positive polarity sentences based on document content.  ...  the regulatory procedure parameters weighted binary artificial neural network model, we will provide an algorithm to classify content.  ...  The study presents a proposed algorithm using neural network model able to offer a similarly for data mining algorithms to classify text phrases polarity document.  ... 
doi:10.36953/ecj.2015.se1633 fatcat:z66kftgvibhqbnzdltgyxe4uqi

Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications [article]

Kayvan Bijari, Hadi Zare, Emad Kebriaei, Hadi Veisi
2019 arXiv   pre-print
Eventually, the learned features of the documents are fed into a deep neural network for the sentiment classification task.  ...  Then, we employ a representation learning approach on the combined graphs of sentences to extract the latent and continuous features of the documents.  ...  Afterward, a convolutional neural network is used to learn and classify text graphs.  ... 
arXiv:1902.10247v2 fatcat:qhosgx7bbracnjgvw55gxy76ee

Hierarchical Topic-Based Communities Construction for Authors in a Literature Database [chapter]

Chien-Liang Wu, Jia-Ling Koh
2010 Lecture Notes in Computer Science  
The proposed method applies the CONGA algorithm to discover collaborative communities from the network constructed from the co-author relationship.  ...  In this paper, given a set of research papers with only title and author information, a mining strategy is proposed to discover and organize the communities of authors according to both the co-author relationships  ...  The JSD between B {Neural Network} and B {Data Mining} ACM is lower than that between B {Neural Network} and B {Neural Network} ACM .  ... 
doi:10.1007/978-3-642-13025-0_53 fatcat:b6qivsqbhzhgfnyruzpyjhoi2m

Research on Domain Information Mining and Theme Evolution of Scientific Papers [article]

Changwei Zheng, Zhe Xue, Meiyu Liang, Feifei Kou, Zeli Guan
2022 arXiv   pre-print
How to effectively use the huge number of scientific papers to help researchers becomes a challenge.  ...  representation learning of scientific and technological papers, the field information mining of scientific and technological papers, and the mining and prediction of research topic evolution rules of  ...  neural networks [2][3] , transportation networks began to use graph neural networks to capture spatial features, and the study of smart transportation has gradually become a hot topic.  ... 
arXiv:2204.08476v1 fatcat:7cte3exhajbilbkvhktjgyvqha

Towards Opinion Summarization of Customer Reviews

Samuel Pecar
2018 Proceedings of ACL 2018, Student Research Workshop  
In this paper, we introduce our research plan to use neural networks on user-generated travel reviews to generate summaries that take into account shifting opinions over time.  ...  It is impossible for any human reader to process even the most relevant of these documents. The most promising tool to solve this task is a text summarization.  ...  Instead of a feed-forward neural network a recurrent neural network (RNN) was used. RNN emphasizes the order of input words.  ... 
doi:10.18653/v1/p18-3001 dblp:conf/acl/Pecar18 fatcat:5d3p4w6gvvfixdvp2jpcq6l4ia

A Group Recommendation System of Network Document Resource Based on Knowledge Graph and LSTM in Edge Computing

Yuezhong Wu, Qiang Liu, Rongrong Chen, Changyun Li, Ziran Peng, Xiaolong Xu
2020 Security and Communication Networks  
This paper proposes a group recommendation system for network document resource exploration using the knowledge graph and LSTM in edge computing, which can solve the problem of information overload and  ...  The experimental results show that the proposed system recommends network document resource more accurately and further improves recommendation quality using the knowledge graph and LSTM in edge computing  ...  Guezouli [30] used the correlation characteristics of neighbor nodes in the neural network to combine all documents into a neural network and retrieves the most relevant document according to query.  ... 
doi:10.1155/2020/8843803 fatcat:eeezr7ijhbbs3abj36d2th7z44

Research on Communication Resource Management System based on Metamaterials

Zhou Rong, L. Zhao, A. Xavior, J. Cai, L. You
2017 MATEC Web of Conferences  
By using the electromagnetic simulation method, the triple band and tunable transmission/reflection characteristics are investigated.  ...  Process neural network (PNN) is a development of traditional neural network in the time domain.  ...  And then reconstructed signal which has been fitted into time-varying functions is used as the input of process neural network.  ... 
doi:10.1051/matecconf/201710004045 fatcat:eck7va7qnrdmhllsp5zo4ttape

Review of Improvement of Web Search Based on Web Log File

Soniya P. Chaudhari, Prof. Hitesh Gupta, S. J. Patil
Some important method based on sequential pattern Mining. Some are based on supervised learning or unsupervised learning. And also used for other method such as Fuzzy logic and neural network  ...  Therefore this model uses neural network of Olfa Nasraoui and Mrudula Pavuluri introduced in 2004. Then neural network will be used to determine dissimilarities.  ...  CONCLUSION Web log is used to improve local search especially the cluster session and neural network.  ... 
doi:10.24297/ijct.v3i2b.2880 fatcat:ahsgjqqxyncgpdnuisybk25vja

Project Documentation for Android

Amra Ruman, Geetha M S, Gowthami K S, Nethravathi R
2017 IJARCCE  
Use case diagram Sequence diagram We are using PostgreSQL [4][16] database to store the data and perform various actions on it.  ...  provide an easier way of referencing various projects, technical papers, articles, technical articles and journals related to the current technical trend thus helping the students in their technical documentation  ...  An artificial neural network (ANN), often just called a "neural network" (NN), is a mathematical model or computational model based on biological neural networks, in other words, is an emulation of biological  ... 
doi:10.17148/ijarcce.2017.6451 fatcat:hp2ig3d7tbcwxpttjsp6obbmle

Mining top-k Popular Datasets via a Deep Generative Model

Uchenna Akujuobi, Ke Sun, Xiangliang Zhang
2018 2018 IEEE International Conference on Big Data (Big Data)  
In this paper, we focus on the problem of extracting top-k popular datasets that have been used in data mining, machine learning, and artificial intelligence fields.  ...  We solve this problem on an attributed citation network, which includes node content information (text of published papers) and paper citation relations.  ...  For example, a paper can fit into both graph mining and neural networks subfield category if it applies neural networks to graph problems.  ... 
doi:10.1109/bigdata.2018.8621957 dblp:conf/bigdataconf/Akujuobi0Z18 fatcat:urgvwtoujjggrfh5kj553xxyq4

Process mining classification with a weightless neural network [article]

Rafael Garcia Barbastefano and Maria Clara Lippi and Diego Carvalho
2020 arXiv   pre-print
Using a weightless neural network architecture WiSARD we propose a straightforward graph to retina codification to represent business process graph flows avoiding kernels, and we present how WiSARD outperforms  ...  the classification performance with small training sets in the process mining context.  ...  Considering the application of neural networks in mining processes, it is always desirable to have both a clear preprocessing step as well as the use of a small subset of training data.  ... 
arXiv:2009.12416v1 fatcat:lrlpzc4n2nhfdb4wv2zpe7v55y

Bert-Enhanced Text Graph Neural Network for Classification

Yiping Yang, Xiaohui Cui
2021 Entropy  
Recently, graph neural networks (GNN) have exhibited some excellent properties in textual information processing.  ...  To solve these problems and comprehensively utilize the text's structure information and semantic information, we propose a Bert-Enhanced text Graph Neural Network model (BEGNN).  ...  Data Availability Statement: The source code and the datasets used in the experiments is available at, accessed on 24 July 2021.  ... 
doi:10.3390/e23111536 pmid:34828233 pmcid:PMC8624482 fatcat:amxsi3dy4fd45mruo5kg6xy7eu

Mining and searching association relation of scientific papers based on deep learning [article]

Jie Song and Meiyu Liang and Zhe Xue and Feifei Kou and Ang Li
2022 arXiv   pre-print
Therefore, the research on mining and searching the association relationship of scientific papers based on deep learning has far-reaching practical significance.  ...  Existing methods generally use the graph network structure to mine associations.  ...  Traditional deep convolutional networks such as convolutional neural networks [71] and recurrent neural networks [72] are no longer applicable, but graph convolutional networks [73] can capture graphs  ... 
arXiv:2204.11488v1 fatcat:zxwvpnids5bopberzumjofgupq
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