Intelligent Text Mining Model for English Language Using Deep Neural Network

Today there exist various sources that provide information in very massive amount to serve the demand over the internet, which creates huge collection of heterogeneous data. Thus existing data can be categorized as unstructured and structured data. In this paper we propose an idea of a tool which intelligently preprocesses the unstructured data by segmenting the whole document into number of sentences, using deep learning concepts with word2vec [11] and a Recurrent Neural Network [13]. At the beginning step we use word2vec which was introduced by Tomas Mikolov with his team at Google, to generate vectors of the inputted text content which will be further forwarded to Recurrent Neural Network. RNN takes this series of vectors as input and trained Data Cleaning Recurrent Neural Network model will perform preprocessing task (including cleaning of missing, grammatically incorrect, misspelled data) to produce structured results, which then passed into automatic summarization module to generate desired summary.