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Natural Language Processing (NLP) Optimization

A chatbot’s ability to consistently understand and interact with a user is dictated by the robustness of the Natural Language Processing (NLP) that powers the conversation. Kore.ai’s Platform uses a unique Natural Language Processing strategy, combining Fundamental Meaning and Machine Learning engines for maximum conversation accuracy with little upfront training. Bots built on Kore.ai’s Platform can understand and process multi-sentence messages, multiple intents, contextual references made by the user, patterns and idiomatic sentences, and more. The NL engine includes recognition support for a wide range of entities and provides the tools needed to further customize your bot’s language understanding using additional patterns.

Optimizing your Bot for Natural Language Understanding

To make sure your Bot is NLP-optimized, you can define, and refine names and terms used for your bot to enhance the NLP interpreter accuracy and performance to recognize the right Bot task for the user. You begin by defining synonyms at the task level, and then manage and refine synonyms, and test at the Bot level.

To get started optimizing your bot and bot tasks, you need to open the Natural Language page and click the relevant tab. To open the Natural Language page, open the bot that you want to optimize NLP settings for, and on the left-hand navigation panel of the bot, click Natural Language.

  • Training – In the Training section, you can test how the NLP interpreter recognizes and responds to user input for a Bot, and then if needed, train the interpreter to recognize the correct user intent. For more information, see Training Your Bot.
  • Machine Learning – With Machine Learning, you can enhance Bot recognition of user utterances for better recognition and system performance for the user intent, which is the intended task that the user wants to access. For more information, see Machine Learning.
  • Synonyms – You can use the Synonyms section to optimize the NLP interpreter accuracy in recognizing the correct task and task field provided by the user for the names of your tasks and task fields. For more information, see Managing Synonyms.
  • Patterns – In the Patterns section, you can define slang, metaphors, or other idiomatic expressions for task names and task fields. For more information, see Managing Patterns.
  • Standard Responses – Standard responses are pre-defined text responses to users based on an event, condition, trigger, or user input. In the Standard Responses section, you can modify existing bot responses, or add additional responses for the same event.For more information, see Managing Standard Responses.
  • Ignore Words & Field Memory – In this section, you can configure bot intelligence by persisting data for each task to pre-populate data fields in another related task for the same bot in the Field Memory settings for each task. You can also define words to ignore in user utterances to increase performance and intent recognition. For more information, see Managing Ignore Words & Field Memory.
  • Task Identification Settings – In this section, you can define the recognition confidence levels required for minimum recognition actions, the confidence range for asking a user to choose from a list of possible matches, and a recognition confidence level for a positive match for knowledge tasks. For more information, see Task Identification Settings.

Next Steps

To get started with NLP optimization, review the tasks on the Natural Language tab. To get started, you should test how the NLP interpreter recognizes and responds to user input for a Bot in the Training section.

In addition to the Training section, the following natural language sections are available:

To learn more about best practices and tips for optimizing NLP, see the Natural Language Processing Guide.

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