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Evaluating Natural Language Understanding Services [pdf]

• valentinvieriu 9y ago
  Just in time. I was just researching this today. Quick question ( maybe not related). Is there a good open source solution for Entity Extraction, and more exactly mapping it to Wikipedia, ( Like GOOGLE NATURAL LANGUAGE API ) ?
  • jjwiseman 9y ago
    Rasa, evaluated in the paper, can do entity extraction. Other open source libraries that can do entity extraction: OpenNLP (https://opennlp.apache.org/docs/1.5.3/manual/opennlp.html#tools.namefind.recognition https://opennlp.apache.org/docs/1.5.3/manual/opennlp.html#to...), Stanford NLP (https://nlp.stanford.edu/software/CRF-NER.shtml https://nlp.stanford.edu/software/CRF-NER.shtml), MALLET (http://mallet.cs.umass.edu/ http://mallet.cs.umass.edu/),
    • valentinvieriu 9y ago
      Maybe my question was misleading. I'm more interested, for my current project on mapping those entities with Wikipedia articles ( see https://goo.gl/mNdDRD https://goo.gl/mNdDRD ) or try the https://cloud.google.com/natural-language/ https://cloud.google.com/natural-language/ example
• jjwiseman 9y ago
  "From a high-level perspective, LUIS performed best with an F-score of 0.916, followed by RASA (0.821), Watson Conversation (0.752), and API.ai (0.687). LUIS also performed best on each individual dataset: chatbot, web apps, and ask ubuntu. Similarly, API.ai performed worst on every dataset, while the second place changes between RASA and Watson Conversation."