The large volumes of structured, as well as unstructured data generated at an unprecedented rate, has taken the industry by storm. Growing prevalence of Big Data in the IT sector has fueled the need for newer tools powered by Artificial Intelligence (AI) and Natural Language Processing (NLP).
Have you ever wondered if the person you have been venting your frustration on or cribbing about the inefficient services rendered to you is a chatbot or a human? Being greeted by an automated voice prompt, designed on an algorithm to answer a limited set of questions, is a thing of the past. Interactive chatbots have become the rage of the town.
With the development of NLP, communication with machines is no longer a far-fetched idea. From Apple’s Siri, Microsoft’s Cortana, to Amazon’s Alexa, their ability to interpret and respond to the users in their everyday language is possible with the inclusion of NLP in their interface. The famous article, “Computing Machinery and Intelligence”, published by Alan Turing in 1950 proved to be the turning point in the conceptualization of machine translation and artificial intelligence. Advancements in technology have led to the development of novel features, thus bridging the gap in communication between humans and computers.
NLP processes the everyday human dialect into machine language by combining artificial intelligence and computer science with computational linguistics. This technology extracts meaningful and useful data from the unstructured data generated and performs tasks such as automatic translation, speech recognition, analysis of the sentiment based on the tone, relationship extraction, summarization, and others.
The acceptance of NLP has led to the enhanced customer experience. Growing adoption of Machine to Machine technology and a remarkable increase in the volume of data generated has fueled the need for NLP enabled devices and technologies. By leveraging NLP, organizations avail high efficiency with advanced features such as speech processing, text mining, sentiment analysis, and others.
The technology includes rule-based, hybrid, and statistical natural language processing. It finds applications in numerous sectors, ranging from BFSI, automotive, healthcare, manufacturing, oil and gas, and the media and entertainment industry. Latest innovations and developments, along with the mergers and acquisitions between the major players, propel the growth of the market.
With technological advancements, NLP is now used in the oil and gas industry for the identification and assessment of risks. By analyzing the enormous amounts of historical data, NLP supplements unstructured data with labels, thus making it suitable for usage along with the structured data.
In other news, Zoom.AI announced the acquisition of the Toronto-based company, SimplyInsight. Users can use their native language to ask questions via chat. The acquisition will give Zoom.AI access to the acquired company’s NLP patents and thus add to their natural language processing abilities built in-house.
Use of Natural Language Processing in the Oil And Gas Industry
Making its presence felt across a wide range of sectors, NLP has gradually helped the oil and gas industry achieve feats that seemed humanly impossible. Analyzing documents containing vital statistics, problems encountered, causes of failure, and the solutions implemented, as well as the preventive measures that were undertaken can be a tedious task. The information contained in documents are unstructured forms of data that include images, charts, numbers, and texts.
Extracting useful and meaningful material from the historical data is now possible owing to the adoption of NLP. The technology helps the industry achieve continuous operability with maximum efficiency and minimize risks associated with safety. The data hidden in emails, audio files, notes, and other forms can give critical insights into the details of production and planning, reservoirs, causes of accidents, among others aid in the optimal operation of the industry. The working conditions have improved remarkably and are much safer, owing to the extraction of important logs and accounts of injuries, accidents, and other factors that hinder the workflow.
Text-Data Processing Breakthrough With Cortical.io Retina Engine
The Cortical.io Retina engine is anticipated to put an end to the age-old problem of text-data processing faced by large enterprises. Built on an algorithm based on the working of the human brain, the engine processes information on the same lines.
Cortical.io Retina engine analyzes the similarities not just between words but also sentences constructed using different words, but having the same meaning. The technology gives accurate results irrespective of the language evaluated. The stand-alone system provides structured data when fed unstructured or semi-structured information present in contracts and other documents.
The information acquired aids in the business performance analysis processes. The primary technology used is based on the semantic folding theory on which the human brain processes unstructured data and classifies them according to their types.Cortical.io retina engine first gathers information from the reference literature and further cuts the text into slices based on the content. The slices, known as snippets, are then placed on a 2D grid, with the similar snippets located close to each other. Each snippet distributed in the grid is assigned a pair of coordinates, making it easier to analyze the meaning of words. The system identifies words and sentences having a larger number of common active bits as similar information.
About the author
Meghnaa Menon is a writer/researcher at Progressive Markets. She holds a bachelor’s degree in Computer Engineering and uses her technical background to write on diverse topics, including current trends in the software and related sectors.