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 access the Natural Language options. These options are categorized under various headings for your convenience:
- Training – In the Training section, you can define how the NLP interpreter recognizes and responds to the user input for a Bot, and then train the interpreter to recognize the correct user intent.
- Machine Learning Utterances – 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.
- Synonyms & Concepts – You can use the Synonyms section to optimize the NLP interpreter accuracy in recognizing the correct intent and entity provided by the user.
- Patterns – In the Patterns section, you can define slang, metaphors, or other idiomatic expressions for intent and entities.
- Thresholds & Configurations – Using 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.
- Modify additional settings like auto training setting for user utterances and negative intent patterns.
- Intelligence – This section helps enhance the bot capabilities beyond its defined tasks. This section helps you guide the bot behavior in deviations and disruptions.
- Manage Interruptions allows you to define how interruptions identified during ongoing task execution should be handled.
- Amend Entity enables the user to amend requests and defines the bot behavior in such scenarios.
- Configure bot to allow identification of multiple intents when present in the user utterance.
- Modify additional advanced settings like subdomains, bot contact card, and Kore.ai identity requirements.
- Default Conversations – This section allows one to defines responses to standard queries, bot events and when an intent is not understood.
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:
- Machine Learning – For more information, see Machine Learning.
- Synonyms – For more information, see Managing Synonyms.
- Patterns – For more information, see Managing Patterns.
- Standard Responses – For more information, see Managing Standard Responses.
- Ignore Words & Field Memory – For more information, see Managing Ignore Words & Field Memory.
- Task Identification Settings – For more information, see Task Identification Settings.
To learn more about best practices and tips for optimizing NLP, see the Natural Language Processing Guide.