RASA NLU gives developers an open source solution for natural language processing

That’s done (usually) using the Hidden Markov Models system (HMM). The team agrees that right now we are struggling to find good use cases for bots. It doesn’t take a genius to realize that even the best conversational AIs available today are little more than glorified voice-activated remote controls.

That means that not only are we still learning about NLP but also that it’s difficult to grasp. NLP then allows for a quick compilation of the data into terms obviously related to their brand and those that they might not expect. Capitalizing on the uncommon terms could give the company the ability to advertise in new ways.

RASA won’t solve this, but it might make it easier for an unconventional player to get into the game. LASTMILE’s team is based in Berlin, Germany but Weidauer and his co-founder Alan Nichol hope their project can bolster the entire bot ecosystem. To date, LASTMILE has raised seed capital from Techstars and a few angels. In addition to RASA, the group has a dedicated product for enterprise customers.

What Is Natural Language Processing And What Is It Used For?

As mentioned above, natural language processing is a form of artificial intelligence that analyzes the human language. It takes many forms, but at its core, the technology helps machine understand, and even communicate with, human speech. For folks who don’t spend a lot of time with engineers, APIs allow developers to rapidly create products without having to reinvent the wheel. Natural language processing, i.e converting human language into something a computer can understand, is pretty difficult but incredibly necessary for creating bots.

But, if you’re looking to push a bot to market you probably want more ownership over your product. I might not touch on every technical definition, but what follows is the easiest way to understand how natural language processing works. Every day, humans say thousands of words that other humans interpret to do countless things.

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At its core, it’s simple communication, but we all know words run much deeper than that. There’s a context that we derive from everything someone says. Whether they imply something with their body language or in how often they mention something. While NLP doesn’t focus on voice inflection, it does draw on contextual patterns.

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When explaining NLP, it’s also important to break down semantic analysis. It’s closely related to NLP and one could even argue that semantic analysis helps form the backbone of natural language processing. RASA NLU, a new open source API from LASTMILE, supports developer’s bot efforts by reducing the barriers to implementing natural language processing. 25 companies have been using RASA NLU in closed beta, but now everyone will be able to access the libraries on Github.

I probably got more pitches for bot startups than anything else. If we hope to break beyond the rigid functionality of today’s tools, a prerequisite is going to be giving bot developers a bit more open source love. The HMM uses math models to determine what you’ve said and translate that into text usable by the NLP system. Google, Netflix, data companies, video games and more all use AI to comb through large amounts of data.

If the HMM method breaks down text and NLP allows for human-to-computer communication, then semantic analysis allows everything to make sense contextually. Semantic analysis is how NLP AI interprets human sentences logically. When the HMM method breaks sentences down into their basic structure, semantic analysis helps the process add content. Next is the actual understanding of the language and context. Each NLP system uses slightly different techniques, but on the whole, they’re fairly similar. The systems try to break each word down into its part of speech (noun, verb, etc.).

• If the HMM method breaks down text and NLP allows for human-to-computer communication, then semantic analysis allows everything to make sense contextually.
• The systems try to break each word down into its part of speech (noun, verb, etc.).
• To date, LASTMILE has raised seed capital from Techstars and a few angels.
• Voice-based systems like Alexa or Google Assistant need to translate your words into text.

It’s no surprise then that businesses of all sizes are taking note of large companies’ success with AI and jumping on board. Some forms of artificial intelligence are more useful than others. The first step in NLP depends on the application of the system. Voice-based systems like Alexa or Google Assistant need to translate your words into text.

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The reason I’ve chosen to focus on this technology instead of something like, say, AI for math-based analysis, is the increasingly large application for NLP. Wit.ai is already free and doesn’t place any limits on requests. Most of the other natural language APIs are free for tinkerers at the start and only start charging after a predetermined number of requests.