How Does Natural Language Processing API Work?

How Does Natural Language Processing API Work?
How Does Natural Language Processing API Work?
Spread the love

Are you a computer science graduate? If yes, then chances are high that you might have already encountered the term – Natural Language Processing or NLP. The thing is, NLP is not only a part of computer science but also artificial intelligence and human language as well. This is the very technology that acts as the driving factor behind the machine’s ability to interpret, understand, analyze, and manipulate human languages. 

If you are someone who was experiencing issues in understanding how a natural language processing API works, then this article will be quite valuable for you. 

Why is an NLP API Vital?

One of the top reasons behind the vitality of natural language processing is its capacity to evaluate vast volumes of text data present in social media comments, customer support tickets, online reviews, news reports, and so on. Every business data is packed with invaluable insights. And, with the help of a paid or free NLP API, assessing the insights becomes extremely easy. Businesses can quickly discover what those data are meant for. 

It processes by making the machine comprehend and make sense of what all the human language is trying to say in a swifter, more precise, and more uniform approach as compared to human agents. 

The NLP AI API operates by functioning data in real-time as well as throughout the day. This approach is also incorporated into the data to promise that the results received are equally authentic. The moment the NLP API perceives the piece of text and their emotions, businesses can begin prioritizing and arranging data in a way suiting their needs. 

See also  What Are The Top 6 Reasons Your Car Ac Leaks Water?

How Does Natural Language Processing API Work?

A natural language processing API works by categorizing the human language into fragments to easily recognize if the sentences have grammatical coherence, analyze the meaning of words, and understand the context as well. Through this method, it becomes quite effortless for computers to assess the purpose by reading, and understanding the written text just like humans do.  

Some of the basic NLP API tasks that data scientists perform as pre-processing tasks prior to NLP tools making sense of human language are:

  1. Tokenization: This functions by breaking down the text into tiny parts or single clauses 
  2. Part-of-speech-tagging: This includes marking up the nouns, pronouns, adjectives, verbs, prepositions, adverbs, and so on
  3. Stemming and Lemmatization: This operates by standardizing words by minimizing them to their basic forms 
  4. Stop Word Removal: This involves filtration of the regular words that add on little or no unique information, like prepositions and articles – at, to, a, the, etc   

It is only after post this step that the natural language processing API converts the text into something that the computer can perceive. This step is followed by the creation of the NLP algorithm.   

Natural Language Processing API Algorithms

Once the data has been pre-assessed, the following step involves shifting to the next step which deals with the creation of the NLP algorithm. Additionally, training needs to be conducted that can evaluate the original language and achieve particular tasks. Two major NLP tools that are incorporated to resolve the problems are:

  1. Rule-based method: This technique focus on hand-generated grammatical rules that are required to be conceived by linguists, or knowledge engineers. This method is considered the oldest one in curetting NLP algorithms and is still in use. 
  2. Machine learning algorithms: If you are looking for a model based on statistics then the machine learning model is the one. They are known to carry out work after being trained (training data). 
See also  Using an equipment maintenance log to improve equipment performance

 One of the major reasons behind the selection of machine learning algorithms is their capability to discover on their own. There is no compulsion to lay out manual rules. Instead, computers acquire past information to predict upcoming events. This additionally offers more adaptability. 


Applications of Natural Language Processing API

The best NLP API is valuable for numerous instances and the following describes it perfectly. 

  1. Question Answering: This is one of the applications that focus on creating systems that spontaneously resolve the questions enquired by humans in their own language. 
  2. Spam Detection: The spam identification function is utilized for identifying unnecessary e-mails moving to a user’s inbox.  
  3. Sentiment Analysis: Also known as opinion mining, sentiment analysis is utilized on the web for examining the attitude, behavior, and emotional state of the deliverer. This application is incorporated by showering a combination of natural language processing and statistics by empowering the values of either a negative or positive or neutral text and simultaneously recognizing the mood of the content (sad, angry, happy, and similar ones). 
  4. Machine Translation: This function is used to translate text or speech from one original language to another like Google Translator.  
  5. Spelling Correction: Microsoft Corp offers word processors like MS Word and PowerPoint for rectifying spellings. 

Apart from all these 5 applications, there are more to NLP API like speech recognition, chatbot, information extraction, and natural language understanding, and so on. It is essential to thoroughly understand the natural language processing API by going through some of the best ones. You need to identify which one would you need by trying out some of the free APIs, then proceeding to the paid ones. 

See also  How to Build a Mobile App

Spread the love