Information Extraction from Unstructured data using Augmented-AI and Computer Vision

Process of information extraction (IE) is often used to extract meaningful information from unstructured and unlabeled data. Conventional methods of data extraction including application of OCR and passing extraction engine, are inefficient on large data and have their limitation. In this paper, a peculiar technique of information extraction is proposed using A2I and computer vision technologies, which also includes NLP

Extracting Location Names from Unstructured Italian Texts Using Grammar Rules and MapReduce

Named entity recognition aims at locating elements in a given text and classifying them according to pre-defined categories, such as the names of persons, organisations, locations, quantities, etc. This paper proposes an approach to recognise the location names by extracting them from unstructured Italian language texts. We put forward the use of the framework MapReduce for this task, since it is more robust than a classical analysis when data are unknown and assists at parallelising processing, which is essential for a large amount of data