Automatic Classification of Medicinal Plants of Leaf Images Based on Convolutional Neural Network

With the rapid developments of Internet technology, a mass of law cases is constantly occurring and needs to be dealt with in time. Automatic classification of law text is the most basic and critical process in the online law advice platform. Deep neural network-based natural language processing (DNN-NLP) is one of the most promising approaches to implement text classification. Meanwhile, as the convolutional neural network-based (CNN-based) methods developed, CNN-based text classification has already achieved impressive results. However, previous work applied amounts of manually-annotated data, which increased the labor cost and reduced the adaptability of the approach. Hence, we present a new semi-supervised model to solve the problem of data annotation. Our method learns the embedding of small text regions from unlabeled data and then integrates the learned embedding into the supervised training. More specifically, the learned embedding regions with the two-view-embedding model are used as an additional input to the CNN’s convolution layer. In addition, to implement the multi-task learning task, we propose the multi-label classification algorithm to assign multiple labels to an instance. The proposed method is evaluated experimentally subject to a law case description dataset and English standard dataset RCV1 . On Chinese data, the simulation results demonstrate that, compared with the existing methods such as linear SVM, our scheme respectively improves by 7.76%, 7.86%, 9.19%, and 2.96% the precision, recall, F-1, and Hamming loss. Analogously, the results suggest that compared to CNN, our scheme respectively improves by 4.46%, 5.76%, 5.14% and 0.87% in terms of precision, recall, F-1, and Hamming loss. It is worth mentioning that the robustness of this method makes it suitable and effective for automatic classification of law text. Furthermore, the design concept proposed is promising, which can be utilized in other real-world applications such as news classification and public opinion monitoring.

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A comparative analysis of euphemistic sentences in news using feature weight scheme and intelligent techniques

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211295 ◽

2021 ◽

pp. 1-12

Author(s):

K. Seethappan ◽

K. Premalatha

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Short Term Memory ◽

Automatic Classification ◽

Weighting Scheme ◽

Feature Weighting ◽

Classification Algorithms ◽

Weighting Schemes

Although there have been various researches in the detection of different figurative language, there is no single work in the automatic classification of euphemisms. Our primary work is to present a system for the automatic classification of euphemistic phrases in a document. In this research, a large dataset consisting of 100,000 sentences is collected from different resources for identifying euphemism or non-euphemism utterances. In this work, several approaches are focused to improve the euphemism classification: 1. A Combination of lexical n-gram features 2.Three Feature-weighting schemes 3.Deep learning classification algorithms. In this paper, four machine learning (J48, Random Forest, Multinomial Naïve Bayes, and SVM) and three deep learning algorithms (Multilayer Perceptron, Convolutional Neural Network, and Long Short-Term Memory) are investigated with various combinations of features and feature weighting schemes to classify the sentences. According to our experiments, Convolutional Neural Network (CNN) achieves precision 95.43%, recall 95.06%, F-Score 95.25%, accuracy 95.26%, and Kappa 0.905 by using a combination of unigram and bigram features with TF-IDF feature weighting scheme in the classification of euphemism. These results of experiments show CNN with a strong combination of unigram and bigram features set with TF-IDF feature weighting scheme outperforms another six classification algorithms in detecting the euphemisms in our dataset.

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