The Best Natural Language Processing APIs

Natural language processing (NLP) is a field of computer science that will inevitably affect the ways in which machines and humans interact.

While Star-Trek-like future (with people talking to their houses and spaceships) still looms on the horizon, the communication between people and computers is becoming more and more natural thanks to NLP. In this article, we will go over the existing and most promising NLP APIs that can bring ‘Star Trek’ fiction one step closer to reality.

Different NLP APIs aim to solve different problems. Here are some of the most common tasks in NLP: natural language generation, speech recognition, different kinds of text analysis, topic segmentation, information retrieval, sentiment analysis, analysis and interpretation of unstructured text, speech, translation, querying a database in natural language and other.

The benefits of NLP development are countless. While some are of great importance for improving and evolution of Business Intelligence tools (which we discussed in our previous article), other NLP technologies are indispensable for creating chatbots and virtual agents (or even for a physical robot to create natural conversations between your apps and users)

Big players

So which NLP APIs are on everyone’s lips? Well, this is something that the big players, like IBM, Google, Amazon Web Services (AWS), Microsoft and Facebook are thoroughly working on. And their efforts are not spent in vain:

IBM Watson

IBM Watson is one of Best Self-Service Business Intelligence Tools that surely everyone has heard of IBM Watson is a cutting edge BI tool that combines NLP, machine learning, and real-time computing power to sift through massive amounts of unstructured data—web pages, documents, emails, journals, social posts, and more — to answer questions fast.

IBM Watson is known for its vast NLP APIs collection of smart services designed to help users understand sentiment, keywords, entities and high-level concepts. For example, with the Watson Conversation) service you can easily build and deploy chatbots and virtual agents, convert audio and voice into written text and backwards, and much more.

Google Cloud Natural Language API

It would have been somewhat strange if Google decided to stay away from the hype around NLP. So there you go! Google's Cloud Natural Language API already offers sentiment analysis, entity recognition (landmarks, public figures, products, etc.) and syntax analysis (English, Spanish, and Japanese). It is a fully managed service providing companies with an automated text processing.

This service has already found application in analyzing customer feedback and customer sentiment. Besides, it is a quick and reliable method of extracting metadata, keywords, action items and other key information from various types of unstructured content.

We can’t go without mentioning Google’s Translation API) - a service that can quickly translate text between thousands of language pairs. Just so you know, there are more than 30,000 natural languages in the world, and smartest humans can master up to 32 languages. But whether the need to learn languages will remain in a century or so, is very hard to tell.

Amazon Lex

One of the most powerful players in AI and NLP is Amazon Web Services (AWS). They have recently released Amazon Lex) Amazon Lex, the technologypowering Amazon's virtual assistant Alexa, that enables you to quickly build sophisticated, natural language, conversational bots. The product covers a whole range of NLPtechnologies, including automatic speech recognition and natural language understanding, allowing for a highly engaging user experience and lifelike conversational interactions.

Microsoft Cognitive Services

Microsoft Cognitive Services - another big player on the market – also has a wide range of APIs allowing your apps to process natural language, evaluate sentiment and topics, and learn how to recognize users’ intentions. Moreover, the Services’ APIs can solve tasks such as intelligent recommendations and semantic search.

Facebook

This social network giant is not staying aside of the NLP technology revolution. So, for instance, DeepText is Facebook's futuristic text understanding engine. With near-human accuracy it can understand the textual content of several thousands posts per second, spanning more than 20 languages. Although Facebook is by far not the only company working on deep learning and language understanding, it does indeed have a huge advantage over the others with 1.6 billion users posting and commenting on their website every single second, Facebook has a unique set of data to which it can apply its expertise.

As far as big players are concerned, for now it is looking more like a competition for who can expand technological horizons faster. No matter the outcome, for users this is a win-win situation.

Apart from major players, there are dozens of smaller companies and open source APIs that are notable for their flexibility, attention to customers’ needs or narrow specialization in either of the fields. These Lexalytics, aylien.com, Automated Insights, Indico, MeaningCloud, Rosette text analytics, Wit.ai and many others.

A good example of a targeted NLP-product is cTAKES (clinical Text Analysis and Knowledge Extraction System), used in healthcare. This is an open-source project for information extraction from electronic health record clinical free-text. It processes clinical notes, identifying types of clinical named entities — drugs, diseases/disorders, signs/symptoms, anatomical sites and procedures.

NLP for database - New way of dealing with data

Here’s another example of targeted NLP application that we’d like to cover in more detail: databases. Although this is not something huge corporations focus on, it is still a very promising field. Let us try | and explain where the demand is coming from.

Everyone knows that Database Administrator’s job is tough. No action goes without applying hero-like skills, such as the knowledge of database languages like SQL. There is, however, a growing need for non-techie people to be able to retrieve information from databases without having to write SQL queries. For senior management, for example, this will result in faster and more data-driven business decisions whereby they don’t have to wait for their IT department to translate the computer ‘Abracadabra’ into something more human-like.

NLP-to-database solution helps access information stored in a database using natural language interface, either by typing their query or simply asking the database. Seems like magic at first, but it’s only the beginning of a new NLP era that is likely to change our lives upside down.

There are already several products on the market that do this (we are going to review them in our next article). Among them is FriendlyData - a pioneering API-based Natural Language Processing solution for databases. Here is a quick comparison table that demonstrates ways of doing things before and after FriendlyData.

Old wayNLP for Database
Want to make sense of your data? Learn SQLData made accessible for everyone, even non-technical people.
Need to build smarter APPs? Gain NLP and Data Science expertise.No NLP specialist required. Launch products faster and add advanced NLP functionality just using API
Time to make informed business decisions? Wait a couple of days for IT analysts to interpret the data for you.Anyone involved in the business process can form a database query and have full and quick access to information.
Ready for exploring data? Use filters, browsing categories and adjust search settings.Google-like search experience of working with structured data

NLP will reshape the world of technology

NLP technology is experiencing a huge evolutionary leap and is continuously bringing humans closer and closer to digital data.

As we mentioned earlier, the human civilization has produced over 30,000 natural languages which is only 10 (!) times more than the number of computer languages! Not to mention the vast amount of libraries and platforms that programmers have to grasp before they can actually communicate with machines.

NLP’s primary focus lies on bridging this communication gap by integrating both natural and virtual worlds, along with its other facilities like search, merge, comprehension, etc.This leaves no doubt that NLP will reshape the world of technology.

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