How intelligent automation can bridge the gap between unstructured data and effective information The best of enterprise solutions from the Microsoft partner ecosystem

Natural Language Processing Consulting and Implementation

nlu and nlp

As the names suggest, NLU focuses on understanding human language at scale, while NLG generates text based on the language it processes. Natural language processing has two main subsets – natural language understanding (NLU) and natural language generation (NLG). This is thanks to machine learning (ML), which is software that can learn from its past experiences — in this case, previous conversations with customers. When supervised, ML can be trained to effectively recognise meaning in speech, automatically extracting key information without the need for a human agent to handle conversations.

Today’s machines can analyse more language-based data than humans, without fatigue and in a consistent, unbiased way. Considering the staggering amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to fully analyse text and speech data efficiently. Your device activated when it heard you speak, understood the unspoken intent in the comment, executed an action and provided feedback in a well-formed English sentence, all in the space of about five seconds. The complete interaction was made possible by NLP, along with other AI elements such as machine learning and deep learning.

NLU feedback loop​

Using Natural Language Understanding to automatically categorise interactions has multiple benefits. Greater consistency, a deeper insight into what customers are asking about and improved efficiency as it removes administration. As NLG technologies improve basic categorisation could evolve into summarising the entire call and adding it to the customer’s record.

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In recent years, natural language processing has contributed to groundbreaking innovations such as simultaneous translation, sign language to text converters, and smart assistants such as Alexa and Siri. Natural language processing (NLP) is the technique to provide semantics to information extracted from optical character recognition engines and documents. In this report, we progress from understanding the mechanics of extracting data from unstructured documents with image recognition towards a deeper understanding of information understanding through NLP. We will look at the use cases in insurance, challenges, and tools and application.

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Instead of searching a specific document or email chain for Biotech, workers can search for sector tags. Perhaps another sector is commonly mentioned along with biotech, serving as an avenue of potential insight. Conversely, one might wish to find all price movements in an email chain or set of 15,000 news stories, regardless of the direction and specific vocabulary used (surge, spike, jump, skyrocket, shoot up, etc.). Statistical language processingTo provide a general understanding of the document as a whole. Raw language processingAs raw data varies from different sources, we bring content processing services to ensure your data is enriched for the highest-quality results.

  • Automated encounters are becoming an ever bigger part of the customer journey in industries such as retail and banking.
  • In this post, we are defining NLP, NLU, and NLG to highlight the differences between them.
  • For example, sentiment analysis training data consists of sentences together with their sentiment (for example, positive, negative, or neutral sentiment).
  • Through continuous feeding, the NLP model improves its comprehension of language and then generates accurate responses accordingly.
  • Although these technologies are not new, the increasing quality and value that they provide to businesses has improved significantly and are playing a major role in understanding management information.
  • The primary role of NLG is to make the response more fluid, engaging, and interesting as an actual human would do.

Discourse integration looks at previous sentences when interpreting a sentence. Read and interpret highly-curated content, such as documentation nlu and nlp and specifications. Identify potential fraud and risk by analyzing financial and contract documents as well as specific communications.

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