Correlation between Image and Text from Unstructured Data Using Deep Learning

Information extraction (IE) is a technique for extracting structured data or knowledge from unstructured data by determining the references to words as well as the relationships between them. IE systems can be used to directly extract knowledge from a text corpus, or to extract useful data from a pile of documents which moreover, can be further analyzed with deep learning algorithms to discover more patterns. In today's era, the internet exhibits several characteristics including unstructured data and noisy text information. Unstructured data may contain audio, image, text, video, etc. Information extraction can be useful for topic detection (Twitter, Digital libraries), Breast Cancer detection and many more applications. The proposed system can exploit deep learning for information extraction using image as well as text and can significantly improve its performance with the inclusion of structured and unstructured data. This paper focuses on extraction of information from unstructured data using various deep learning models and concepts of Natural Language Processing (NLP), Long Short Term Networks (LSTM) and for topic extraction used Latent Dirichlet Allocation (LDA) etc.