A Tool to Extract Name Entity Recognition From Big Data in Banking Sectors

Abstract

Generally, the Internet is the global system of interconnected computer networks, connecting millions of computers as well as people, and thus generates a massive quantity of information on a daily basis. This leads to extracting the necessary information using information filtering (IF) in several domains. In our implementation, the named entity recognition (NER) technique is employed to automatically extract valuable data from the unstructured natural language texts. As several works has been outlined in detecting named entities, plenty of very different NER tools exist for several domains. However, NER remains a giant challenge so to solve this problem we proposed a novel framework by combining three efficient classifiers. This article proposes a three-layered neural network approach with conditional random field (CRF), the Pachinko allocation model (PAM), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) for detecting named entities in three steps. First, a classifier based on CRF is employed to train the input file. Second, PAM is employed to boost the previous output created by CRF to enhance the label annotation. Third, the ANFIS captures the deep features of the information by itself from the pre-trained information to attain accurate predictions. Experimental results show that the learned model yields a banking domain with a recall rate of 92%, a precision rate of 95% and F-measure of 92% by implementing it in the R Platform.