(An early design for an intelligent chatbot has changed a lot in practice.)

The paper

It is mainly divided into three parts:

  • SuperRobot framework body.
  • Natural language understanding systems.
  • Word vector Trainer.

SuperRobot

  • AliceBot is responsible for small talk, uses AIML Engine and belongs to Rulebased.
  • Chatty statements are entered into DB behind the scenes.
  • ServiceBot was responsible for business QA and Matcher was responsible for matching.
  • Custom QA pairs are entered into the DB through background input.
  • AbilityBot is responsible for interacting with third-party systems, using REST to request natural language understanding systems to return intents and entities.
  • The background can define interactive service interfaces.
  • Develop in Java.

Natural language understanding systems

  • Mainly responsible for the work of intention prediction and entity recognition.
  • The entity and intent of the business data sample that needs to be annotated.
  • The entity extractor trains the model through sequence annotation and realizes entity extraction.
  • Intention classifier trains the model through SVM and realizes intention prediction.
  • MIT NLP tool MITIE.
  • Developed in Python, C++.

The word vector Trainer

  • Mainly responsible for word vector training.
  • Common corpus includes Wikipedia, Baidu Encyclopedia.
  • Business corpus includes business system data.
  • Use the DLib machine learning library.
  • Develop in C++.
  • The trained model is for previous use.

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My 2017 article summary – Natural Language Processing

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