Evaluation of ML and Advanced Deep Learning Text Classification Systems

Preview this chapter:

Evaluation of ML and Advanced Deep Learning Text Classification Systems, Page 1 of 1

< Previous page | Next page > /docserver/preview/fulltext/9789815305395/chapter-7-1.gif

Classifying texts into groups determined by their content is called text classification. In this process, automatic labelling of documents written in natural languages is carried out according to predetermined labels. Both text comprehension systems, which perform transformations on texts such as creating summaries, answering queries, and extracting data, and text-retrieving systems, which obtain texts in fulfillment of a user query, rely heavily on text categorization. In order to learn effectively, current algorithms for supervised learning for text classification need a large enough training set. This research introduces a novel text categorization algorithm that uses artificial intelligence techniques (machine studying and deep learning techniques) and needs fewer documents for training than previous methods. To generate a feature set from already-classified text documents, we resort to "word relation," or association rules based on these words. To classify the data, we use the idea of a Convolutional Neural Network with Deep Convolution to the extracted features and then employ a single genetic algorithm approach. The suggested method has been built and thoroughly tested in a working system. The results of the experiments show that the suggested system is an effective text classifier.