Utilizing Amazon Comprehend for Natural Language Processing applications

Natural Language Processing is as broad a topic as words themselves, and as such, there is far too much information to cover in one article. This article is the second part in a series discussing the value of Natural Language Processing. Read part one here.

Between phone calls, electronic communications, or written words, the prevalence of language is evident. Given our reliance on a digital business environment, understanding the context of words is even more important for operations to succeed. Businesses that can extract information from these types of data in a quick and systematic way will fully be able to harness customer insights and market trends while making business decisions. Leveraging natural language processing and Amazon Comprehend in your business operations can help your organization grow in the current digital landscape.  

What is Natural Language Processing?

Natural Language Processing (NLP) is a category of artificial intelligence designed to listen, read, understand, and generate natural language. The most common use case is natural language understanding; where a model is designed to read a piece of text and extract information be it categorizing the text, extracting the entities discussed in the text, or the sentiment of the text. NLP’s biggest value in business is its ability to gain insights into unstructured data that previously would be too much of a burden to assess.

What is Amazon Comprehend?

Amazon Comprehend is an Amazon Web Services product that provides a pre-trained, machine learning NLP model that includes out of the box natural language understanding and custom model creation to help businesses uncover insights from their written text data. These out-of-the-box implementations are:

• Entity extraction
• Key phrase
• Language identification
• Personally identifiable information
• Sentiment
• Syntax

These out-of-the-box models require zero training and only requires formatting the text correctly and uploading to AWS. This allows for a simple path to analyzing unstructured and unlabeled text data.