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. See here for an overview.
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 Virtual Assistant
To make sure your VA is NLP-optimized, you can define, and refine names and terms used for your assistant 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 Virtual Assistant and 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 VA, and then train the interpreter to recognize the correct user intent.
- Machine Learning Utterances – With Machine Learning, you can enhance 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 & Rules – In the Patterns section, you can define slang, metaphors, or other idiomatic expressions for intent and entities.
- Thresholds & Configurations – 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 the knowledge graph.
- Modify Advanced Settings like auto training setting for user utterances and negative intent patterns.