Natural Language Processing: Taking Your Business to the Next Level
Natural language processing is a type of AI that assists programs in understanding and interpreting human language. (Source: Service Express)
In this edition of Voices of the Industry, Jim Carson, Data Science Manager at Service Express, shares how natural language processing techniques can automate tasks and increase accuracy in your organization.
The future of the data center will rely heavily on artificial intelligence (AI) and machine learning (ML) to improve business processes. As mentioned in our previous article, Streamlining Data Center Tasks With Machine Learning, many CIOs and technology leaders are already adopting an AI strategy in their IT departments. One ML technique that stands out as a focus of recent adoption in the data center due to its unique capabilities to analyze unstructured text data is natural language processing (NLP).
According to IBM’s Global AI Adoption Index, around half of organizations are using applications powered by NLP and over a quarter expect to implement them over the next year. The pandemic proved to be a catalyst that accelerated business plans to leverage these opportunities. With thoughtful planning, large amounts of data, and the right models, NLP can help IT departments streamline processes, remove human error and improve the customer experience.
Natural language processing is a type of AI that assists programs in understanding and interpreting human language. NLP fills the gap between human communication and computer understanding. NLP techniques typically analyze large bodies of unstructured text data, including documents, log files, transcripts, etc. The output of an NLP model can vary depending on the desired results. For example, Amazon built Alexa to identify speech patterns and infer meaning or complete a task to assist the user. Recent advances have enabled the NLP field to evolve from tools like spell check to more sophisticated apps such as customer service chatbots, real-time voice-to-text translators, Google Assistant and more.
NLP has the potential to accelerate and automate tasks in your IT department so you can streamline solutions. Examples include:
When it comes to NLP, there is no cut-and-dry formula. You have the autonomy to create a model that supports your company and its unique needs. We often see companies invest in expensive software to analyze their data to determine various outcomes. By leveraging your proprietary data with a customized NLP model, you can tailor the results to your business needs far more closely than a pre-packaged solution.
With open-source software, your team can create the same ML and NLP models the software companies are pitching. Instead of settling for a premade solution, we made our own to help engineers provide a quick and reliable resolution for our customers.
