Commonly known as NLP, Natural Language Processing became a component of Artificial Intelligence able to understand human language as it is spoken or written.
NLP is all about text, including translation and speech. We specifically address these topics in the dedicated Best Machine Translation APIs and Best Speech-to-Text APIs 2022 articles. Here, we focus on NLP AIs that allow the extraction of information from text, also called Text Mining, following with a few examples below.
NER (Named Entities Recognition) consists of recognizing Named Entities in a corpus and assigning them a category. For instance, an algorithm using NER could be able to differentiate and label the two instances of “green” in the sentence “Mrs Green had green eyes” as two separate entities —a Lastname and a color. If you're interested in NER, you might want to read our Top 10 NER APIs.
Data Anonymization is the process of protecting sensitive information by changing/deleting what may connect a living individual to the data.
Language Detection (or language guessing) is the algorithm for determining which natural language the given content is in. For more information, have a look at our Top 10 Language Detection APIs.
Question Answering: Based on a set of documents, the process generates an answer to a given question. This is useful for question-answering applications on sources of truth, like company documentation or a knowledge base.
Sentiment analysis API extracts sentiment in a given string of text. Also called "opinion mining", the technology identifies and detects subjective information from the input text. Eden AI can help you find out which Sentiment Analysis API to choose for your project.
Summarization selects the most relevant information from a text and automatically writes a summary to sum up what it is about.
Keyword Extraction is used to define the terms that represent the most relevant information contained in a text or a document. If you're wondering how to choose and access the right engine according to your data, you might be interested in our Top 10 Keyword Extraction APIs.
While comparing the APIs, it is crucial to consider different aspects, among others, cost security and privacy. NLP experts at Eden AI tested, compared, and used many NLP APIs of the market. Here are some actors that perform well (in alphabetical order):
For all the companies who use Natural Language Processing in their software, cost and performance are real concerns. The NLP market is quite dense and all those providers have their benefits and weaknesses.
Performances of NLP vary according to the type of data used by each AI engine for their model training: AI engines are usually trained with specific data. This means that some NLP APIs may perform great for some languages but won’t necessarily for others.
Natural Language Processing APIs perform differently depending on the language of the audio. In fact, some providers are specialized in specific languages. Different specificities exist in Region specialties: some NLP APIs improve their machine learning algorithm to make them accurate for text in a specific language. For example, some NLP APIs perform well in translating English (US, UK, Canada, South Africa, Singapore, Hong Kong, Ghana, Ireland, Australia, India, etc.), while others are specialized in Asian languages. Rare language specialty: some Natural Language Processing vendors care about rare languages and dialects. You can find NLP APIs that allow you to process text in Gujarati, Marathi, Burmese, Pashto, Zulu, Swahili, etc.
When testing multiple Natural Language Processing APIs, you will find that providers' accuracy can be different according to text quality and format. For example, some NLP APIs perform better with text coming from tweets, others perform better with text from scientific papers, others with text from customer reviews, etc. This can be explained because of the quality of the text (we can imagine that tweets are lower-quality texts compared to scientific papers or press articles for example).
Some NLP APIs are trained with specific data. This means that NLP APIs will perform better for text in the medical field, while others will perform better in the automotive field. If you have customers coming from different fields, you must consider this detail and optimize your choice.
Companies and developers from a wide range of industries (Social Media, Retail, Health, Finances, Law, etc.) use Eden AI’s unique API to easily integrate NLP tasks in their cloud-based applications, without having to build their own solutions.
Eden AI offers multiple AI APIs on its platform amongst several technologies: Text-to-Speech, Language Detection, Sentiment analysis, Keyword Extraction, Summarization, Question Answering, Data Anonymization, Speech recognition, and so forth.
We want our users to have access to multiple NLP and NER engines and manage them in one place so they can reach high performance, optimize cost and cover all their needs. There are many reasons for using multiple NLP APIs:
Eden AI has been made for multiple AI APIs use. Eden AI is the future of AI usage in companies. Eden AI allows you to call multiple AI APIs.
You can see Eden AI documentation here.
The Eden AI team can help you with your Natural Language Processing integration project. This can be done by :
In this article, we explain how the mapping between the input language and the languages supported by the providers is performed to facilitate access to one of our AI engines.