The technological development history is damaged in such a way that the world is full of examples where technological developments are fighting to dominate each other. Some times, these format wars occur tremendously. In short, we can say that the fittest will survive. The innovation which has much power will sustain in this technological development world. For instance, if we have to buy an optical disk, everyone will go for BLU-RAY.
The document has been acquired. This is where CDA answers the following questions: • What is this document or email about? To make sense of the document and the information contained within, it must be cognitively transformed--that is, translated into structured, business-consumable content required by downstream processes and systems (such as BPM, CRM, ECM, ERP, etc.). Cognitive document automation uses a variety of artificial intelligence (AI) capabilities, such as natural language processing (NLP) and machine learning, to cluster, classify, separate, OCR, extract, and understand (human language) any type of document. Machine learning is a key component of CDA, easing the configuration and maintenance of CDA systems.
Machine Learning (ML), along with the Internet of Things (IoT) seems to be the next big revolution in science and technology. AI experts are debating why machine learning is the most wondrous thing, today. They are trying to predict the way ML can affect the future and its evolution. The ability to feed the machine with big amounts of data, so that the machine can learn concepts and rules to focus on specific categories of problems and solutions, is a critical part of AI development. In 1959, the term'machine learning' was coined by Arthur Samuel, an AI professional.
This paper describes USI Answers -- a natural language question answering system for enterprise data. We report on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system's interpretation of the query, and allows easy query adjustment and reformulation.
I review current statistical work on syntactic parsing and then consider part-of-speech tagging, which was the first syntactic problem to successfully be attacked by statistical techniques and also serves as a good warm-up for the main topic-statistical parsing. Here, I consider both the simplified case in which the input string is viewed as a string of parts of speech and the more interesting case in which the parser is guided by statistical information about the particular words in the sentence. Finally, I anticipate future research directions.