Hi Aaron Thanks very much for your reply and hints!
I will a have closer look at the LightSwitch example and the best matching algorithm. Thanks Michael Am Sa., 15. Mai 2021 um 04:57 Uhr schrieb Aaron Radzinski < [email protected]>: > Michael, > Welcome to the dev list and NLPCraft! > > Short answer: > ------------------ > These two sentences get parsed into the similar set of tokens (based on > the model [1]) which match the same intent - hence the same action for both > sentences. > > NOTE: if you want to play with NLPCraft make sure to run from 'master'. > Project is actively being developed and official releases lag behind. > > Longer answer: > -------------------- > If you run LightSwitch example you can look at the data probe log out and > see very detailed output for all different parsing variants. You will > notice that NLPCraft automatically filters out stop words, detects > user-defined named entities from [1] (often via multiple synonyms), and > find the best matching intent. One of the key aspects of NLPCraft is the > fact that it does not require any classic ML learning (corpus development, > prep & training) - it only requires a simple, deterministic model [1] and > thus providing the deterministic answers. You can also see at the start of > the data probe that it reports over 13K unique synonyms - all auto-derived > from the same model [1], so it provides very deep "comprehension" for the > this subject domain (light switch operation). > > Hope it helps, > > 1. > https://github.com/apache/incubator-nlpcraft/blob/master/nlpcraft-examples/lightswitch/src/main/resources/lightswitch_model.yaml > > On Fri, May 14, 2021 at 1:11 AM Michael Wechner < > [email protected]> wrote: > >> Hi Together >> >> I have just noticed the NLPCraft Project and it sounds very interesting! >> >> I am an ASF member since 2004, whereas I have become very interested in >> NLP/NLU recently and would be happy to contribute in the future if >> possible. >> >> To start with, I would be curious to understand how NLPCraft is detecting >> similar sentences, like for example >> >> "Turn the lights off in the entire house" >> and >> "Turn off all lights now" >> >> ? >> >> All the best >> >> Michael >> >
