Natural Language Processing, or NLP, performs the same functions for text as Object Recognition does for photos and videos. From unstructured text, NLP extracts structured data. This implies that text sent to NLP algorithms from a Speech Recognition algorithm, a report, or a free-form email can have meaning extracted from it.
How does one go about doing this? Supervised learning is used to train NLP algorithms. They receive a lot of categorized material, which they might utilize to learn specifics about the newly presented text. NLP can be used to classify unstructured material, such as emails that are free-form, by comparing word patterns in the text to its training set. Natural Language Processing (NLP) also faces a domain difficulty. An algorithm’s training set may be restricted to medical papers if it must parse records from medical records. An NLP algorithm’s training set needs to be much bigger, though, if it is to be able to parse a wide variety of document formats. In this instance, context becomes crucial. Is the user referring to lettuce when they input the word “produce” or are they constructing something? Are they referring to their motorized scooter or acting depressed when they type “moped”? For NLP algorithms to accurately decipher textual meaning, they must be able to recognize the context of individual sections.
Numerous business-related uses of natural language processing exist. Text classification or tagging is the initial step. An NLP algorithm can determine whether a news piece is about politics, business, sports, etc. if it is given to it. It wouldn’t use keywords to achieve this. Rather, it would provide a comprehensive representation of what an article about politics, for instance, might look like using its training data set. The system will classify as political every new article it evaluates that shares similarities with the items in its training set that have been marked as being about politics.
Additionally, named entity extraction using NLP is possible at a finer degree of detail. It may identify individuals, locations, things, quantities, and dates within a text passage, rather than classifying a complete article. “This article was written in 2018 by Victor, a Los Angeles resident and employee of Foundation AI.” The identified entities in this case would be “Victor,” “Foundation AI,” “Los Angeles,” “article,” and “2018.” Similar to Object Recognition, this work can be completed by pre-trained Named Entity recognition algorithms; but, in order to ensure accuracy, these algorithms must be fine-tuned and trained by seasoned data scientists. By training these algorithms on particular information domains (legal, medical, etc.), their accuracy can be increased.
For instance, an email sent to a firm can be scanned and categorized, tagged to be given to the appropriate person inside the company, and the sender’s information can be extracted and recorded into the ticketing system, CMS, or CRM of the business by combining these two processes. The email can be automatically forwarded and the sender’s details automatically entered into the business database when combined with a Robotic Process Automation (RPA) system. In a later post, we’ll go over how AI may be utilized to increase the functionality of RPA systems.
Understanding language is an issue that Natural Language Processing tackles. After you’ve comprehended a client’s request, you might want to employ an RPA system, as we’ve just covered, to carry out the activity, or you might want to ask the consumer for further information or give them information that could be helpful. We will go into more depth about Natural Language Generation in a later essay, but these actions necessitate its use.