Four years ago, we hacked 3rd party commands into Siri without jailbreaking before Alexa Skills or SiriKit were released (https://www.wired.com/2014/04/googolplex/). It was the first App Store for voice commands. Since then, we’ve worked on NL interfaces at Google and Apple Siri. Now we're tackling the next problem: products using NLP are fairly simplistic in what they can do for users. For example, systems like Siri still struggle to directly answer a basic question like "When is the Y Combinator application due?" because it can't understand and reason where the answer may lie in a sentence on Y Combinator's website.
We’re approaching the problem differently by understanding the structure of language and relationships within text, instead of relying on more simplistic methods like keyword matching. We build a graph of entities and their relationships within a sentence along with other linguistic information. You can think of it as “Open Information Extraction” with a lot more information (https://www.plasticity.ai/api/demo).
Currently, we use a TensorFlow model to perform classical tasks like parts of speech, tokenization, and syntax dependency trees. We built our own Wikipedia crawler for data to better handle chunking and disambiguation, which helps return more accurate results for multi-word entities in sentences like: "The band played let it be by the beatles." We wrote our open IE algorithms from scratch, focusing on speed. It's written completely in C++ and we are adding more features everyday.
Our public APIs are in beta right now, we’re constantly working to improve the accuracy, and we’re looking forward to hearing feedback. We’d love to hear what the HN community is working on with NLP and how we can help!
by Andrey
Azimov