What is Natural Language Processing API with Example?
Let us talk about natural language processing today. Almost every other day, we encounter a machine that easily perceives human languages. We wonder to ourselves, how revolutionary technology has become that it has led to this upgraded change. One of the quintessence ideally describing the enhancement is natural language processing. Till the recent time, we couldn’t imagine a machine paying heed to our voice commands. But today, almost every other thing that we use is affixed with that. We no more have to manually instruct a machine to perform an activity.
The perfect way to understand natural language processing is with the help of our current-day Alexa. We ask countless questions, enquire about the weather, ask it to solve problems, play a song, set an alarm, and so on.
Comprehensive Understanding of Natural Language Processing
The computer science students must already be excited since they are well aware of this answer. Natural language processing is a branch of computer science, or to be precise, a branch of artificial intelligence or AI. The sole focus of this branch is enabling computers competent enough to perceive and act instructions much like humans in reality do.
What does natural language processing do? It merges computational linguistics, and methodical structuring of human language that includes statistics, machine learning, and deep learning models. When amalgamated, these technological advances authorize computers to operate the human language in a textual or voice data form. To fully comprehend the instruction, one understands the speaker or writer’s sentiment or intent needs to be there.
What is Natural Language Processing API?
After having a considerable understanding of natural language processing, let’s dive into the idea of an API. Natural language processing API is technically a machine learning tool pre-coached to perform activities like assessing the tone of a specified text. Additional functionalities of a natural language API comprise analysis of syntax, entity, and content classification. Starting from voice-operated GPS systems, to digital assistants, to speech-to-text dictation software to customer service chatbots, this API is almost omnipresent.
Features of the Best NLP API
Numerous features govern the operations of an NLP AI API. Quite some approaches employ the performance breakdown and annotation of the text. Every specific level of this analysis offers priceless information for a thorough understanding of the language. The approaches are explained below.
- Sentiment analysis: This particular method examines the text, and recognizes the prevalent emotional opinion present within the text. This is conducted specially for examining a writer’s attitude as positive, negative, or neutral. This approach is assessed through the ‘analyze sentiment’ method.
- Entity analysis: This approach detects the text for identical entities like proper nouns, common nouns, prepositions, clauses, and so on. It then follows it up by reverting that information about the entities and recognizing the dominant emotional opinion of the entity present in the text. This again is examined to understand the writer’s attitude towards the entity as a positive or negative or neutral one. Entity analysis is conducted by implementing the ‘analyzeEntitySentiment’ method.
- Syntactic analysis: It functions by extracting the linguistic information, segregating the provided text into a series of sentences and tokens (primarily, word boundaries), and further offers an assessment of those tokens. This function is performed with the help of the ‘analyzeSyntax’ method.
- Content Classification: This function operates by examining text content and reverts with a content category for the latter. This function works by incorporating this method of ‘classify text’.
Every API, even the free NLP API call also recognizes and reverts the language in case the language isn’t specified by the caller in the initial request.
Examples of the Best Natural Language Processing API
An NLP API certainly saves time and money by conducting indexing and similar tasks swiftly and efficiently. Some of the top APIs are stated below.
- Google NLP
- Text Processing
- Text Mining and NLP
- Language Tool
- Language Tool
- Text Classification
- Sentiment Analysis
- Topics Extraction
Availability of Free NLP APIs
This is one of the top highlights of the complete NLP API article. Not everyone can pay for the premium APIs to access natural language processing. Therefore, open-source NLP APIs come in handy. They are not only free of cost but also offer convenient customization.
Who Needs NLP APIs?
Here comes a million-dollar question, who exactly needs an NLP API? These people are developers interested to conduct machine translation, language research, news analysis, noun phrase extraction, part-of-speech tagging, document indexing, topic modeling, sentiment analysis, stemming and lemmatization, named entity identification, or classification. If you are a developer wanting to perform any of these, simply opt for a natural language processing API.
A natural language API is undoubtedly a beneficial way to find numerous inquiries. All you need to do is, conduct your research and locate the API that fits you the best. It is always beneficial to start by opting for a free plan, and then upgrading it according to your needs.