The Guide to Natural Language Processing with API

July 16th, 2021

Reading time about 13 min

Application Programming Interface or API has become one of the most prominent types of software, and is used by most websites and applications. The main characteristic of an API is to add more features to software and in turn, improve the utility of the software. Being flexible and easy-to-use. API is desired by the most. There is a plethora of API tools that add various features to website and natural language APIis one among them. As the name suggests the tool adds Natural Language Processing functionality to your website.

What is Natural Language Processing?

Natural Language Processing is an Artificial Intelligence technology that enables computers to comprehend both the text, as well as spoken words just as people understand them. In simpler terms, computers will be able to understand the context and the emotion that people are trying to communicate through words. This is an advanced version of text analysis, where machines analyze textand try to understand sentimentsfrom them.

Natural Language Processing

How Does NLP Work?

While creating NLP APIs demand a lot of knowledge related to computer science, understanding how it works is quite easy. A computer takes in various input just as humans do. For example, being humans, we listen to sounds with our ears, while computers do so by using a microphone. In the same way, the sound that we hear is processed in our brains, while computers use various codes and algorithms to do so. There are four basic steps involved in Natural Language Processing:

  1. Tokenization: Throughtokenization, we can simplify complex sentences, and make it easier to analyze. As we know, computers use a different language to communicate with us, and therefore, having complex sentences may cause problems to the system during further analysis.
  2. Parts-of-speech Tagging: The part-of-speechtagging is the process in which the machine marks the words in the sentence as subject, verb, adverb, object, and so on.
  3. Lemmatization and Stemming: In this step, the computer converts all the words to its basic forms. In other words, the suffixes of the words are removed and only the root word is retained in this process. For example, the word agreeable is converted to agree by removing the “-able” suffix.
  4. Removing Stop Words: Stop words are those which are used mostly in a sentence and does not convey any actual meaning. This includes words like a, an, the, is, so, etc. This step filters all such words thus making the sentence ready to be processed.

Uses of Natural Language Processing API

The development of deep learning has led to massive breakthroughs in the field of AI. Owing to this, most programs that use machine learning models have become a lot more useful as they can perform complex tasks. NLP is one among the models that became powerful with the help of deep learning. Some of the notable uses of NLP APIs are:

  • Language Translators: Language translators are a product of Natural Language Processing. These programs have the ability to analyze text in a particular language and convert it into the language specified by the user. To do so, these translators must do all the processes involved in NLP and make the text suitable for processing. This is why language translators need Natural language processing.
  • Analyzing Reviews: Companies value customer reviews more than anything and especially today where customer service plays a major role in the success of business, reviews in social media, news article, e-commerce platforms, etc., are constantly monitored. Most companies use various NLP tools to analyze reviews and group them based on positive, negative, and neutral comments. To achieve this, they use a method called sentiment analysis, where the tool identifies various sentiment analysis entities or words that give a certain sentiment to the comment.
Natural Language Processing API

For example, “The customer service was great”, in this comment the tool analyses the word “customer service” and then looks for the sentiment entity, which in this case is the word “great”. With this information the tool classifies this comment as a positive comment on customer service.

  • Grammar Checking Tools: When writing a piece of content like a blog post or an article, one has to make sure to write without grammatical errors. Grammar tools can help you do this easily and efficiently. The grammar checkers have to scan your text document for which they need an NLP API. As NLP allows the tool to understand the text, rectifying grammatical errors becomes much easier.
  • Voice Assistants:Voice assistants are found in every piece of technology from a watch to a car. For a voice assistant to function well, they need to be able to understand what the person is saying and must be able to find valid answers to his or her questions. This is where NLP functionality comes in. It lets the assistant understand the user by converting audio to text, and give answers by converting text to audio.
  • Helps in Recruitment: When a company opens for recruitment, it takes a lot of CVs. However, the company does not have the time or the resources to manually check each and every CV and find the right candidate for the interview. The Natural Language Processing can help you in this as it is very good when it comes to text and content classification. It can search for specific fields in the CV such as qualification, skills or any other field and find the right qualities that suit the company, which is then classified as accepted.

These are some of the common situations where natural language APIs are used. There are other uses to this software, and more improvements are yet to come, along with the development of learning modules and machine learning algorithm.

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