What is Natural Language Processing (NLP)?
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 APIs2022 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.
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Examples of Natural Language Processing tasks
Named Entity Recognition
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
Data Anonymization is the process of protecting sensitive information by changing/deleting what may connect a living individual to the data.
Language Detection
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
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
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
Summarization selects the most relevant information from a text and automatically writes a summary to sum up what it is about.
Keyword Extraction
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.
Top Natural Language Processing APIs on the market
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):
Alibaba Cloud
Allganize
AWS
Azure
Cloudmersive
Emvista
Google Cloud
IBM
Intellexer
Lettria
Meaning Cloud
Open AI
Paralleldots
Phedone
Textrazor
Tisane AI
Twinword
Performance variations of NLP APIs
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.
Performance variations according to the languages
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.
Performance depending on the quality of text
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).
Performance depending on the industry
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.
Why choose Eden AI to manage your NLP APIs
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.
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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:
Fallback provider is the ABCs. You need to set up an NLP API that is requested if and only if the main NLP API does not perform well (or is down). You can use confidence score returned or other methods to check provider accuracy.
Performance optimization. After the testing phase, you will be able to build a mapping of NLP vendors’ performance that depends on the criteria that you chose (languages, fields, etc.). Each data that you need to process will then be sent to the best NLP API.
Cost - Performance ratio optimization. You can choose the cheapest NLP provider that performs well for your data.
Combine multiple AI APIs. This approach is required if you look for extremely high accuracy. The combination leads to higher costs but allows your AI service to be safe and accurate because NLP APIs will validate and invalidate each other for each piece of data.
How Eden AI can help you?
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.
Centralized and fully monitored billing on Eden AI for all NLP APIs
Unified API for all providers: simple and standard to use, quick switch between providers, access to the specific features of each provider
Standardized response format: the JSON output format is the same for all suppliers thanks to Eden AI's standardization work. The response elements are also standardized thanks to Eden AI's powerful matching algorithms.
The best Artificial Intelligence APIs of the market are available: big cloud providers (Google, AWS, Microsoft, and more specialized engines)
Data protection: Eden AI will not store or use any data. Possibility to filter to use only GDPR engines.
You can see Eden AI documentation here.
Next step in your project
The Eden AI team can help you with your Natural Language Processing integration project. This can be done by :
- Organizing a product demo and a discussion to better understand your needs. You can book a time slot on this link: Contact
- By testing the public version of Eden AI for free: however, not all providers are available on this version. Some are only available on the Enterprise version.
- By benefiting from the support and advice of a team of experts to find the optimal combination of providers according to the specifics of your needs
- Having the possibility to integrate on a third-party platform: we can quickly develop connectors