Natural Language Processing

What is Natural Language Processing?

Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that deals with applying linguistic and statistical algorithms to text to extract meaning from human language.

NLP incorporates a combination of computational linguistics, which involves constructing models based on the rules of human language, along with statistical, machine learning, and deep learning models. These technologies collectively enable computers to process human language in various forms such as text or voice data, allowing them to grasp the complete meaning, including the intent and sentiment of the speaker or writer.

Natural Language Processing automates the reading of text using sophisticated speech recognition and human language algorithms. NLP engines are fast, consistent, and programmable, and can identify words and grammar to find meaning in large amounts of text. Furthermore, NLP is increasingly playing a significant role in enterprise solutions that streamline business operations, enhance employee productivity, and simplify critical business processes.

Our NLP offering

Valuable information contained in emails, customer messages and chat, claims, contracts, and any other documents is often a source of untapped potential as companies struggle to transform this unstructured data into the kind of actionable, structured data that is easily manageable by bots. 80%-90% of enterprise data is unstructured; it’s critical for organizations to be able to understand, analyze and use it to enable a real intelligent automation across the entirety of an enterprise data assets. AI-based NLP capabilities of the expert.ai hybrid natural language Platform provide accuracy, contextual understanding and flexibility to accelerate and improve organizations’ data automation strategies. data automation strategies.

By adding NLP/NLU to RPA, enterprises now have the ability to increase the flexibility and scalability of automation, expanding deployment to more complex use cases and business processes by making sense of unstructured language data.

Reveal Group supports organizations in making RPA bots more effective to immediately solve pain points dealing with routine tasks and activities, and by enabling the intelligent automation of more complex and strategic language-intensive processes. NLP outputs — including intent, automatic categorization, emotional and behavioral traits identification, entity extraction and sentiment analysis — can be deployed and easily delivered by Reveal Group to automate multiple use cases, from common cross-industry use cases (email triage in customer services, data analysis, comparison and extraction in legal departments) to more industry-oriented processes (claims management in insurance companies, loan origination and customer onboarding in banking and financial services).

How can NLP benefit you?

Natural language processing is the driving force behind machine intelligence in many modern real-world applications. Here are a few examples:

Spam detection: You may not think of spam detection as an NLP solution, but the best spam detection technologies use NLP’s text classification capabilities to scan emails for language that often indicates spam or phishing. These indicators can include overuse of financial terms, characteristic bad grammar, threatening language, inappropriate urgency, misspelled company names, and more. Spam detection is one of a handful of NLP problems that experts consider ‘mostly solved’ (although you may argue that this doesn’t match your email experience).
Machine translation: Google Translate is an example of widely available NLP technology at work. Truly useful machine translation involves more than replacing words in one language with words of another. Effective translation has to capture accurately the meaning and tone of the input language and translate it to text with the same meaning and desired impact in the output language.
Virtual agents and chatbots: Virtual agents such as Apple’s Siri and Amazon’s Alexa use speech recognition to recognize patterns in voice commands and natural language generation to respond with appropriate action or helpful comments. Chatbots perform the same magic in response to typed text entries. The best of these also learn to recognize contextual clues about human requests and use them to provide even better responses or options over time. The next enhancement for these applications is question answering, the ability to respond to our questions—anticipated or not—with relevant and helpful answers in their own words.
Social media sentiment analysis: NLP has become an essential business tool for uncovering hidden data insights from social media channels. Sentiment analysis can analyze language used in social media posts, responses, reviews, and more to extract attitudes and emotions in response to products, promotions, and events–information companies can use in product designs, advertising campaigns, and more.
Text summarization: Text summarization uses NLP techniques to digest huge volumes of digital text and create summaries and synopses for indexes, research databases, or busy readers who don’t have time to read full text. The best text summarization applications use semantic reasoning and natural language generation (NLG) to add useful context and conclusions to summaries.

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