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A Novel 2D Deep Convolutional Neural Network for Multimodal document categorization

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第7期14卷 720-728页

作者： Abkrakhmanov, Rustam Elubaeva, Aruzhan Turymbetov, Tursinbay Nakhipova, Venera Turmaganbetova, Shynar Ikram, Zhanseri Int Univ Tourism & Hospitality Turkistan Kazakhstan Turkish Univ Khoja Akhmet Yassawi Int Kazakh Turkistan Kazakhstan Zhumabek Akhmetuly Tashenev Univ Shymkent Kazakhstan NCJSC S Seifullin Kazakh Agro Tech Res Univ Astana Kazakhstan BTS Digital Astana Kazakhstan

Digitized documents are increasingly becoming prevalent in various industries. The ability to accurately classify these documents is critical for efficient and effective management. However, digitized documents often come in different formats, making document classification a challenging task. In this paper, we propose a multimodal deep learning approach for digitized document classification. The proposed approach combines both text and image modalities to improve classification accuracy. The model architecture consists of a convolutional neural network (CNN) for image processing and a recurrent neural network (RNN) for text processing. The output features from the two modalities are then merged using a fusion layer to generate the final classification result. The proposed approach is evaluated on a dataset of digitized documents from various industries, including finance, healthcare, and legal fields. The experimental results demonstrate that the multimodal approach outperforms single-modality approaches, achieving high accuracy for document classification. The proposed model has significant potential for applications in various industries that rely heavily on document management systems. For example, in the finance industry, the proposed model can be used to classify loan applications or financial statements. In the healthcare industry, the model can classify patient records, medical images, and other medical documents. In the legal industry, the model can classify legal documents, contracts, and court filings. Overall, the proposed multimodal deep learning accuracy, thus enhancing the efficiency and effectiveness of document management systems.

关键词： Scanned documents classification document categorization artificial intelligence machine learning deep learning

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Bengali text document categorization based on very deep convolution neural network

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 184卷 115394-115394页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Siddique, Nazmul Sarker, Iqbal H. Chittagong Univ Engn & Technol Dept Comp Sci & Engn Chittagong 4349 Bangladesh Univ Ulster Sch Comp Engn & Intelligent Syst Coleraine Londonderry North Ireland

In recent years, the amount of digital text contents or documents in the Bengali language has increased enormously on online platforms due to the effortless access of the Internet via electronic gadgets. As a result, an enormous amount of unstructured data is created that demands much time and effort to organize, search or manipulate. To manage such a massive number of documents effectively, an intelligent text document classification system is proposed in this paper. Intelligent classification of text document in a resource-constrained language (like Bengali) is challenging due to unavailability of linguistic resources, intelligent NLP tools, and larger text corpora. Moreover, Bengali texts are available in two morphological variants (i.e., Sadhu-bhasha and Cholito-bhasha) making the classification task more complicated. The proposed intelligent text classification model comprises GloVe embedding and Very Deep Convolution Neural Network (VDCNN) classifier. Due to the unavailability of standard corpus, this work develops a large Embedding Corpus (EC) containing 969, 000 unlabelled texts and Bengali Text Classification Corpus (BDTC) containing 156, 207 labelled documents arranged into 13 categories. Moreover, this work proposes the Embedding Parameters Identification (EPI) Algorithm, which selects the best embedding parameters for low-resource languages (including Bengali). Evaluation of 165 embedding models with intrinsic evaluators (semantic & syntactic similarity measures) shows that the GloVe model is more suitable (regarding Spearman & Pearson correlation) than other embeddings (Word2Vec, FastText, m-BERT) in Bengali text. Experimental results on the test dataset confirm that the proposed GloVe + VDCNN model outperformed (achieving the highest 96.96% accuracy) the other classification models and existing methods to perform the Bengali text classification task.

关键词： Intelligent systems Natural language processing Low resource language Semantic feature extraction document categorization Deep convolution network

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document categorization Using Graph Structuring 1st

Document Categorization Using Graph Structuring

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1st International Conference on Advanced Computational and Communication Paradigms (ICACCP)

作者： Sarma, Sandipan Saha, Punyajoy Sil, Jaya Indian Inst Engn Sci & Technol Howrah W Bengal India

ISBN: (纸本)9789811082375;9789811082368

This paper proposes a document classification model using feature learning (Coates, Demystifying unsupervised feature learning, 2012) [5] approach based on semantics of the documents. In the learning phase, basic vocabulary (BV) for each document class consisting of nouns has been created by proposing a novel approach. The classification phase searches unique words in the BVs and if found, the corresponding sentence becomes a basic sentence (BS). A tree with unique words of the BS is inserted in the respective forest. Associated words of the children are used to continue the tree formation process until no newnode is generated in the tree. Finally, we assign the test document to a class which has a clearly dominant percentage of sentences in the respective forest. The proposed algorithm is compared with various feature-based classification models and satisfactory performance has been observed.

关键词： document categorization Basic vocabulary Importance function

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document categorization Using Semantic Relatedness & Anaphora Resolution:A Discussion

Document Categorization Using Semantic Relatedness & Anaphor...

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IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)

作者： Dhole, Kaustubh Kohli, Harsh BITS Dept Biol Sci Dept Elect & Elect Engn Pilani Goa Campus Pilani Rajasthan India BITS Dept Comp Sci & Informat Syst Pilani Rajasthan India

ISBN: (纸本)9781467367356

document categorization is the process of assigning pre-defined categories to textual documents. State-of-the art approaches have modelled documents in terms of corpus-length long vectors and viewed the problem only from a syntactic perspective. We develop a general measure to estimate the semantic closeness of documents by utilizing the semantic relatedness of the most discriminative individual words that define the document. Anaphora resolution is used to strengthen the meaning ascribed to each document. Our framework benefits from word semantics and the Wordnet taxonomy thus better capturing the underlying meaning of the text and proves to be a more concise representation than traditional Information Retrieval methods. Having the same representation for documents as well as for a category of documents and associating a measure of semantic closeness paves way for modelling documents into a semantic space where unsupervised approaches can be easily used. We evaluate the performance of our measure by implementing it to categorize news documents into two topics and achieve 81 to 92% accuracy.

关键词： document categorization Semantic Relatedness Anaphora Resolution

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The Effect of Preprocessing on Arabic document categorization

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ALGORITHMS 2016年第2期9卷 27-27页

作者： Ayedh, Abdullah Tan, Guanzheng Alwesabi, Khaled Rajeh, Hamdi Cent S Univ Sch Informat Sci & Engn Changsha 410000 Hunan Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410000 Hunan Peoples R China

Preprocessing is one of the main components in a conventional document categorization (DC) framework. This paper aims to highlight the effect of preprocessing tasks on the efficiency of the Arabic DC system. In this study, three classification techniques are used, namely, naive Bayes (NB), k-nearest neighbor (KNN), and support vector machine (SVM). Experimental analysis on Arabic datasets reveals that preprocessing techniques have a significant impact on the classification accuracy, especially with complicated morphological structure of the Arabic language. Choosing appropriate combinations of preprocessing tasks provides significant improvement on the accuracy of document categorization depending on the feature size and classification techniques. Findings of this study show that the SVM technique has outperformed the KNN and NB techniques. The SVM technique achieved 96.74% micro-F1 value by using the combination of normalization and stemming as preprocessing tasks.

关键词： document categorization text preprocessing stemming techniques classification techniques Arabic language processing

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Aiding Effective Encrypted document Manipulation Incorporated with document categorization Technique in Cloud

Aiding Effective Encrypted Document Manipulation Incorporate...

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International Conference on Information and Communication Technologies (ICICT)

作者： Mathew, Samantha Susan Hafsath, C. A. Ilahia Coll Engn & Technol Dept CSE Muvattupuzha 686673 Kerala India

Cloud computing involves storage as well as usage of computer resources and services online. It has emerged as a promising area for data outsourcing. Since the users data are stored in servers controlled by cloud service provider there are always concerns about the security of the stored data. The data can be encrypted to ensure its security but it will increase the complexity of the data access process. The presence of large amount of data in the cloud necessitates an efficient method for data access. An efficient keyword based search on the encrypted cloud data is proposed here which provides marked improvement over the traditional boolean search approach used in cloud environment. It uses the latest informational retrieval methods for the processing of the documents from the cloud. The homomorphic encryption technique is used for the building of a secure index which has the key role in the search process. The vector space model is employed for the representation of the text document as vector. document categorization technique is incorporated for the easy maintenance and retrieval of the data from the cloud. It handles multiple data owners and users with a facility to store and retrieve multiple data files. The approach also makes sure that the processing on the user side is minimized while updating the existing document set. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Homomorphic encryption Vector-space model Secure Searchable Index document categorization

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Bangla Text document categorization Using Stochastic Gradient Descent (SGD) Classifier 1

Bangla Text Document Categorization Using Stochastic Gradien...

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2015 International Conference on Cognitive Computing and Information Processing (CCIP)

作者： Kabir, Fasihul Siddique, Sabbir Kotwal, Mohammed Rokibul Alam Huda, Mohammad Nurul United Int Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781479971718

This paper describes the Bangla document categorization using Stochastic Gradient Descent (SGD) classifier. Here, document categorization is the task in which text documents are classified into one or more of predefined categories based on their contents. The proposed system can be divided into three steps: 1. feature extraction incorporating term frequency (TF) and inverse document frequency (IDF), 2. classifier design using the Stochastic Gradient Descent (SGD) algorithm by learning the distinct features, and 3. performance measure using F1-score. In the experiments on BDNews24 documents, it is observed that our proposed method provides higher accuracy in comparison with the methods based on Support Vector Machine (SVM) and Naive Bayesian (NB) classifier.

关键词： document categorization Term Frequency-Inverse document Frequency Stochastic Gradient Descent F1-score

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An Efficient Method for document categorization Based on Word2vec and Latent Semantic Analysis 15

An Efficient Method for Document Categorization Based on Wor...

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IEEE International Conference on Computer and Information

作者： Ju, Ronghui Zhou, Pan Li, Cheng Hua Liu, Lijun Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Beijing JingDong Century Trade Ltd JD COM DNN Deep Neural Network Lab Beijing Peoples R China Wuhan TipDM Intelligent Technol Wuhan Peoples R China

ISBN: (纸本)9781509001545

document categorization is the process of classifying documents from many mixed documents automatically, and the main problem is how to express document content in vector space completely. This paper proposes a new model named Latent Semantic Analysis (LSA) + word2vec to categorize documents. This is the first attempt of combining word2vec with LSA at document categorization and it can map document to vector space under the premise of keeping document contents fully. At first, we create a term by document matrix and the element of which is decided by Term Frequency-Inverse document Frequency (TF-IDF) weighting and word vector trained by word2vec. This matrix is a 3-dimensional matrix and it can describe the meaning of every word and the content of every document exactly. Secondly, Singular Value Decomposition (SVD) is executed on the matrix and lower computational complexity is gained from this. The model is named LSA + word2vec. Then, document vector gained from the new model are put into Convolutional Neural Network (CNN) to train. CNN is an efficient deep learning algorithm, which improves the accuracy of classification greatly. We evaluate the performance based on the 20newsgroups corpus. The results show that our new model achieves better effects on document categorization tasks, and the accuracy made about 15% improvement than traditional methods, such as LSA and Vector Space Model (VSM).

关键词： document categorization word2vec LSA CNN

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Aiding Effective Encrypted document Manipulation Incorporated with document categorization Technique in Cloud

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Procedia Computer Science 2015年 46卷 668-675页

作者： Samantha Susan Mathew C.A. Hafsath Department of CSE Ilahia College of Engineering and Technology Muvattupuzha Kerala India 686673

关键词： Homomorphic encryption Vector-space model Secure Searchable Index document categorization

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Proximity-based k-partitions clustering with ranking for document categorization and analysis

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第16期41卷 7095-7105页

作者： Mei, Jian-Ping Chen, Lihui Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China Nanyang Technol Univ Div Informat Engn Singapore 639798 Singapore

As one of the most fundamental yet important methods of data clustering, center-based partitioning approach clusters the dataset into k subsets, each of which is represented by a centroid or medoid. In this paper, we propose a new medoid-based k-partitions approach called Clustering Around Weighted Prototypes (CAWP), which works with a similarity matrix. In CAWP, each cluster is characterized by multiple objects with different representative weights. With this new cluster representation scheme, CAWP aims to simultaneously produce clusters of improved quality and a set of ranked representative objects for each cluster. An efficient algorithm is derived to alternatingly update the clusters and the representative weights of objects with respect to each cluster. An annealing-like optimization procedure is incorporated to alleviate the local optimum problem for better clustering results and at the same time to make the algorithm less sensitive to parameter setting. Experimental results on benchmark document datasets show that, CAWP achieves favorable effectiveness and efficiency in clustering, and also provides useful information for cluster-specified analysis. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Clustering Similarity-based k-Medoids Partitioning document categorization

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