Text Document Classification by using WordNet Ontology and Neural Network

Gawade, Manisha; Mane, Tejashree; Ghone, Dhanashree; Khade, Prasad; Ranjan, Nihar M.

doi:10.5120/ijca2018918229

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Ontology-based dimension reduction methods have become important in classification studies. In a study in which feature dimension reduction was carried out by avoiding word repetition with WordNet, a structure was proposed in which classification was performed with CNN [20]. In another study on this subject, the authors compared Naive Bayes, Jrip, J48 and SVM classification methods with PCA and ontology-based feature reduction methods, and it was seen that ontology-based reduction gave better results than PCA.…”

Section: Feature Dimension Reduction Using Wordnet Ontologymentioning

confidence: 99%

Multi-Class Document Classification Using Lexical Ontology-Based Deep Learning

2023

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With the recent growth of the Internet, the volume of data has also increased. In particular, the increase in the amount of unstructured data makes it difficult to manage data. Classification is also needed in order to be able to use the data for various purposes. Since it is difficult to manually classify the ever-increasing volume data for the purpose of various types of analysis and evaluation, automatic classification methods are needed. In addition, the performance of imbalanced and multi-class classification is a challenging task. As the number of classes increases, so does the number of decision boundaries a learning algorithm has to solve. Therefore, in this paper, an improvement model is proposed using WordNet lexical ontology and BERT to perform deeper learning on the features of text, thereby improving the classification effect of the model. It was observed that classification success increased when using WordNet 11 general lexicographer files based on synthesis sets, syntactic categories, and logical groupings. WordNet was used for feature dimension reduction. In experimental studies, word embedding methods were used without dimension reduction. Afterwards, Random Forest (RF), Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) algorithms were employed to perform classification. These studies were then repeated with dimension reduction performed by WordNet. In addition to the machine learning model, experiments were also conducted with the pretrained BERT model with and without WordNet. The experimental results showed that, on an unstructured, seven-class, imbalanced dataset, the highest accuracy value of 93.77% was obtained when using our proposed model.

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Section: Feature Dimension Reduction Using Wordnet Ontologymentioning

confidence: 99%

Multi-Class Document Classification Using Lexical Ontology-Based Deep Learning

2023

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“…Convolutional Neural Networks are popular for their high performance in image, speech, and audio recognition. The idea of using a CNN as a text classifier is not new 5 . After transforming a document text to vectors or matrices with word embedding, it is relatively easy to play with different architectures (different number of layers and nodes) and incorporate common techniques such as padding and pooling.…”

Section: Cnn and Rnn For Text Classificationsmentioning

confidence: 99%

Taxonomical Modeling and Classification in Space Hardware Failure Reporting

Palacios

Hill

2023

Preprint

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NASA Johnson Space Center has collected more than 54,000 space hardware failure reports. Obtaining engineering processes trends or root cause analysis by manual inspection is impractical. Fortunately, novel data science tools in Machine Learning and Natural Language Processing (NLP) can be utilized to perform text mining and knowledge extraction. In NLP the use of taxonomies (classification trees) are key to the structuring of text data, extracting knowledge and important concepts from documents, and facilitating the identification of correlations and trends within the data set. Usually, these taxonomies and text structures live in the heads of experts in their specific field. However, when an expert is not available, taxonomies and ontologies are not found in data bases, or the field of study is too broad, this approach can enable and provide structure to the text content of a record set. In this paper an automated taxonomical model is presented by the combination of Latent Dirichlet Allocation (LDA) algorithms and Bidirectional Encoder Representations from Transformers (BERT). Additionally, the limitations and outcomes of causal relationship rule mining models, commercial tools, and deep neural networks are also discussed.

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“…In those neuro--fuzzy networks, connection weights and propagation and activation functions differ from common neural networks. Although there are a lot of different approaches [10,11,14,15], we usually use the term neuro-fuzzy Function for approaches which display the following properties: A Neuro-Fuzzy Function depends on a fuzzy machine that has been trained using a neural network-based learning algorithm. The learning procedure works on local knowledge and results in only small variations to the overall fuzzy structure.…”

Section: Time Complexitymentioning

confidence: 99%

A Brief Survey on Data Preprocessing Techniques based on Image Represented by a Fuzzy Function using Neural Network

Ranjan

2023

IJRASET

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Deep neural network recently created offers insight on how high-level picture representation may be automatically learnedin raw pixels. Deep learning with Convolutional Neural Networks has showed significant potential in the classification and improvement of picture, but is frequently unfit for predictive modelling without spatial connections utilizing data. In order to organize high dimensional vectors in a compact way for CNN- based profound learning, we offer a method to representation. This study shows the use of deep neural networks to create a system thatcan identify different texture characteristics.

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Text Document Classification by using WordNet Ontology and Neural Network

Cited by 4 publications

References 11 publications

Multi-Class Document Classification Using Lexical Ontology-Based Deep Learning

Multi-Class Document Classification Using Lexical Ontology-Based Deep Learning

Taxonomical Modeling and Classification in Space Hardware Failure Reporting

A Brief Survey on Data Preprocessing Techniques based on Image Represented by a Fuzzy Function using Neural Network

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