Hi, this is Ajay and Alex, and we’re the founders of Plasticity (<a href="https://www.plasticity.ai/" rel="nofollow">https://www.plasticity.ai/</a>). We're building an API that helps developers create human-like natural language interfaces.<p>Four years ago, we hacked 3rd party commands into Siri without jailbreaking before Alexa Skills or SiriKit were released (<a href="https://www.wired.com/2014/04/googolplex/" rel="nofollow">https://www.wired.com/2014/04/googolplex/</a>). 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.<p>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 (<a href="https://www.plasticity.ai/api/demo" rel="nofollow">https://www.plasticity.ai/api/demo</a>).<p>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.<p>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!
I'm impressed with Cortex - All industry leaders (Google, Siri, Alexa) answer "Who killed John Wilkes Booth" with "Abraham Lincoln," but this gives the correct answer. It shows that it has a deeper understanding of it's data sources.
Something I'll keep my eye on, for sure. In the meantime:<p>It feels like you've reinvented much by writing stuff from scratch. spaCy is fast, has tons of features, commonly updated, free, trained on the Common Crawl corpus. Why not just use that? I'm only curious, not critical.
This passage (from another one of the articles on top of HN right now (<a href="https://www.wired.com/2015/11/null#tpw)" rel="nofollow">https://www.wired.com/2015/11/null#tpw)</a>) generates an error when trying to use the Sapien demo: "As a technology journalist, being a Null has served me rather well. (John Dvorak, you know what I’m talking about!) The geek connotations provide a bit of instant nerd cred—to the point where more than one person has accused me of using a nom de plume to make me seem like a bigger nerd than I am.<p>But there’s a dark side to being a Null, and you coders out there are way ahead of me on this. For those of you unwise in the ways of programming, the problem is that “null” is one of those famously “reserved” text strings in many programming languages. Making matters worse is that software programs frequently use “null” specifically to ensure that a data field is not empty, so it’s often rejected as input in a web form."<p>Maybe it's the use of the word `Null`? Not sure, but love what you're doing and thought I'd let you know about this.
This is really cool. Website design is killer and looks beautiful. I tried "Who married the 51st president?" which didn't work but when I tried "Who married Barack Obama?" it responded correctly.<p>I then tried "Who married the president?" and got the correct responses also.<p>The only thing I would change is at the bottom of the Plasticity demo you should have a big sign up button. And a link to your documentation.
What would a good non-commercial use case of this product be like?
Would it help simplify/understand Terms & Conditions better?
Text summarization?
Very interesting. Needs to account for things like low literacy in certain areas. Medical literacy is a big gap for a lot of people. Asked "What is myocardial infarction? Correct article returned. Asked "What is myocardial infraction?" Got article for Civil infraction. "What is hyperkalemia?" returned a result. "What is high potassium?" No result.
"We're make sense of dark data to help companies in technology, law, medicine, and government extract information from text."<p>Ignore the grammar error, you're helping government extract information from text? Where exactly? Do you mean the NSA? Do you mean helping the government look at public internet written commentary to track citizens?
How is this like, or better than Evi, which has origins in 2007 and is now part of Alexa according to wikipedia?<p><a href="https://en.m.wikipedia.org/wiki/Evi_(software)" rel="nofollow">https://en.m.wikipedia.org/wiki/Evi_(software)</a>