Artificial intelligence (AI) is an umbrella term for the automation of intellectual tasks. Recent advances in statistical machine learning and data collection methods, as well as an unprecedented growth of computational capabilities, have made automation of many repetitive tasks a fast, effective, and affordable alternative to human labor.
Natural Language Processing, or NLP, is a branch of AI that deals with the interaction between computers and humans using the natural languages humans communicate with. The objective of NLP technology is to read and make sense of human languages to add or find value within an unstructured text.
NLP is particularly valuable when companies have lots of unstructured text (big chunks of text, not in a database or well-formatted). You want the ability to quickly determine what’s in the text, save it, and add the right meta tags, so you’re able to accurately answer questions. AI can help extract information “nuggets” from this unstructured data (including HTML and PDF documents).
You should be able to make requests like, “Show me the requirements in this document” or, “Show me areas where you find emails and passwords in this document” and the system will know how to do it.
How does it work?
The process is can be broken down into five simple steps:
- Input unstructured text
- Machine captures information
- Question is asked
- Unstructured text is analyzed (Indexed, searched, and retrieved)
- Machine provides answer
How is it useful?
AI techniques based on NLP can be utilized to mine large unstructured texts to find the content of interest or certain types of content, for example: the risk associated with SEC filings or insurance applications. It can also find requirements in specification documentation or the “needle in the haystack” in deep dark web files. Open QA and related question answering systems also depend on such techniques.
Why should I implement NLP?
With recent growing advancements in business intelligence capabilities, there is a growing number of tasks it no longer makes sense to execute manually. Utilizing NLP can enable time savings and expense reduction when shifting your organization’s data-mining efforts away from manual processes.