3 ms·

never mind

• tinyhouse 8y ago
  It's going to be difficult to get a job in NLP in a decent company without good ML fundamentals and experience with deep learning. Obviously working with toolkits like you mentioned is very important since by building stuff you get to learn the most, esp. in an applied field such as NLP, and there's no reason to do everything from scratch. But in job interviews no one cares if someone has experience using Spacy or any other specific tool.
  • opportune 8y ago
    Maybe my knowledge is out of date then, since I was doing NLP in 2016. I’ll delete my comment but I stand by my opinion that you shouldn’t dive straight into implementing some state of the art ML model without learning the basics of computational linguistics
    • tinyhouse 8y ago
      It's a valid point and it's good to have different opinions! I don't see a reason to delete your comment.
    • bitL 8y ago
      With current transfer learning explosion you can do a state-of-art NLP processing in your app within one month without any prior expertise/deep understanding (yeah, needs a lot of effort but doable).
• aglionby 8y ago
  I'm not sure what you're saying about DL in NLP is completely true. To train e.g. a language model from scratch you'll need a load of data, but building on top of pre-trained models (e.g. BERT, ELMo), or even just using pre-trained word embeddings in your RNN, reduces that a lot. Subsequently using these will require some knowledge of ML, even if just to know what the library code for some model you glue on does.