Domain-Specific Language Processing Mines Value from Unstructured Data


Got unstructured text data?

Check out our guest post about how our Domain-Specific Language Processing (DSLP) and patented Weighted Topic Scoring (WTS) can be your alternative or an adjunct to NLP to mine your business data in real-time, published by KDnuggets, a leading site on AI, big data, data mining, data science and machine learning.

Here’s an excerpt:

“Processing unstructured text data in real-time is challenging when applying NLP or NLU. Find out how Domain-Specific Language Processing can be your alternative or an adjunct to NLP to mine valuable information from data by following your guidance and using the language of your business.

Real-time mining of unstructured textual content isn’t simple. To work effectively, a solution must be fine-tuned to meet your organization’s specific needs and address the quirks in your company’s information. To add value, applications require a vocabulary that accurately captures the definitions, context, and nuance of your business and the way it uses language.

Consequently, unstructured textual data needs to be organized before it can be put to use. That’s a huge challenge. For data-driven systems based on artificial intelligence, machine learning or other advanced applications, natural language processing and natural language understanding are supposed to be the solution, either by themselves or as hybrid models that plug in industry terms or use complex Boolean logic. But none of these are easy to use or implement, and without extensive programming and training, they often process data incorrectly.

There is, however, a simpler approach to addressing these challenges, one that does not require NLP or NLU. It’s called “Domain-Specific Language Processing,” or DSLP, and it uses the language of your business to mine unstructured textual data.”

Click to read the full post.