Natural language processing in artificial intelligence (NLP) has become integral to workplace communication as part of the data-driven intelligence of AI. Initially started as handwritten formulas, which seem to be a complicated technology, has now been streamlined with AI algorithms. Today, applications like Siri or Alexa have become part of daily life that show smart communication technology’s tremendous progress.

NLP shows the importance of natural language processing in artificial intelligence for these voice-activated platforms or chatbots to language translation. Besides, several applications of natural language processing in artificial intelligence clearly indicate how natural language processing in artificial intelligence will shape and improve a new era of communication technology with the power of artificial intelligence, deep learning, and machine learning.

Natural Language Processing definition and its features

So, before we dive into the topic, we need natural language understanding, i.e., an overview of natural language processing? Natural Language Processing (NLP) is an artificial intelligence element that is a combination of AI and linguistics for communicating with machines using natural language. Simple use of NLP is google natural language processing using Google voice search.

Natural Language processing techniques

NLP techniques involves two processes:

– Natural Language Understanding

– Natural Language Generation

Natural Language Understanding

As the process name suggests, Natural Language Understanding tries to extract the meaning of the given text. A text can be ambiguous to the process. However, to convert the text, NLU must understand each word’s nature and structure in the text. To resolve the ambiguity, NLU looks for the following:

– Lexical Ambiguity – To check if the words have multiple meanings

– Syntactic Ambiguity – If the Sentence is having multiple parse trees.

– Semantic Ambiguity – If the Sentence is having multiple meanings

– Anaphoric Ambiguity – Phrase or word though mentioned previously but could have a different meaning.

Using lexicons (vocabulary) and a set of grammatical rules, each word’s meaning is understood in this step.

Natural Language Generation

Natural language generation is a subset of NLP and hard to deal with. In this process, automatic text generation happens from structured data. The text must be in a readable and meaningful format with a combination of phrases and sentences.

It follows three phases:

1. Text Planning – Basic content is ordered into structured data.

2. Sentence Planning – Flow of information is formed using the sentences from structured data.

3. Realization – Grammatically correct sentences are produced to represent text.

Levels of natural language processing in artificial intelligence

Applications of natural language processing in artificial intelligence

Chatbots

Implementations of NLP for Chatbots are on the rise. NLP is used to analyze the input language of the customers to the chatbots. Based on the analysis, they provide appropriate responses to the customers or reroute the query to the relevant pages. Using NLP, chatbots streamlines the incoming queries and allows visitors to access relevant information from pages. The entire task happens almost instantaneously, which undoubtedly adds value to both business and customer end related to the communication.

Related post – Top 15 Chatbot Platforms revolutionizing the business world

Talent Acquisition

Talent acquisition is one of the industries which is significantly driven by people and communication. So, it is an unmatched choice for AI-driven communication where accuracy related to the talent pool is an essential factor. Not to mention, NLP plays an important role in increasing the process accuracy in acquisition activities. Not only accuracy but also NLP in chatbots applications helps applicants easily access job descriptions, make queries, schedule interviews, and many more.

NLP in Big Data

IDC recently forecasted that “the amount of analyzed data ‘touched’ by cognitive systems will grow by a factor of 100 to 1.4 ZB by 2025“. This will impact thousands of industries and companies around the globe. It is estimated that 80 % of total data comes in the raw format, and Big Data works as the savior to extract meaningful information from this unstructured data. Big data also means analyzing data, and NLP helps harness the pattern of such huge unstructured data. NLP initiates better interactions between customer communication-related applications. For example, customer calls are better handled and resolved with it.

Similarly, for BI purposes, NLP can search all operations against an input provided in a natural language. Also, sentimental analysis is another area where NLP can provide significant results.

Faster diagnosis

In some scenarios, NLP works as an effective solution to determine the specific diagnosis of any disease. Natural language processing in artificial intelligence can determine the right diagnosis path from the unstructured medical report. For example, NLP software proved to be a good identifier of breast cancer risk in hospitals that use natural language processing to indicate a specific diagnosis from mammography and reports. This, in turn, decreases the need for unnecessary risks of doing biopsies and expedites the treatment procedure.

Customer Review:

As mentioned in the earlier section, sentimental analysis is an important application of natural language processing in artificial intelligence. For example, NLP can easily gather product reviews from a website, and through it, a business can easily analyze the market trend and customer preferences. Besides, it reveals the important status of the business, which helps to improve customer experience.

Final thought

The importance of Natural language processing in artificial intelligence is expected to dominate human-to-machine interaction. Robotics, the financial sector, smart homes, and healthcare are areas where NLP will harness unstructured data and make it more meaningful. It will undoubtedly be a shift from data-driven to intelligence-driven platforms and work as a powerful base for faster business decision-making.