Incorporate NLP into Your Processes

NLP Combines AI and Deep Learning with Linguistics to understand unstructured text, taking contextual nuances into account.

The use of NLP tools to interpret, analyze, and understand your data is typically one step in your overall automation workflow.

NLP Logix provides the understanding on how the pieces fit together to achieve the most valuable insights from your data. With 4 patents in the areas of Data Extraction, Computer Vision, and the ability to interpret data, NLP Logix is a leader in Natural Language Processing within Data Capture Automation.

• Data Capture Automation Data Capture Automation, text is captured in context, much the way a human would read a document. This enables NLP Logix to process non-standard, messy, or overprinted documents. Data can then be extracted and properly routed. Humans would potentially only need to handle the exceptions.

• Predictive Search Results Search engines use NLP to present relevant results based on learned search behaviors. If you get even close to the spelling of a word, the search engine can interpret what you meant to type. If you key in three digits, the search engine is likely to assume you are looking for the location of an area code, a flight number will provide flight status, and a ticker symbol will return stock price results.

• Matching Similar Texts NLP can be used to identify the similarity between texts. Rather than relying on boolean search to identify if two documents contain the same word or string of words, NLP Logix can create a metric to measure the extent of the overlap between multiple texts.

• Sentiment Analysis Sentiment Analysis can be used to determine if data is positive, negative, or neutral. This Natural Language Processing technique is especially helpful for businesses processing large quantities of customer feedback via surveys, reviews, online comments/articles, or other sources.

• Natural Language Chatbots Chatbots that incorporate Natural Language Processing are capable of understanding text and answering appropriately. Thanks to Machine Learning, they can also learn over time to progressively improve their responses. Available 24/7, they are best used for routine questions, routing difficult queries to the human agents.

How will you use Natural Language Processing?