The key for a conversational bot to understand human interactions lies in its ability to identify the intention of the user, extract useful information from their utterance, and map them to relevant actions or tasks. NLP (Natural language processing) is the science of deducing the intention(Intent) and related information(Entity) from natural conversations.
Multi-Pronged Approach to NLP
The Kore.ai Bots Platform employs a multi-pronged approach to natural language, which combines the following two models for optimal outcomes:
- Fundamental Meaning: A computational linguistics approach that’s built on ChatScript. The model analyzes the structure of a user’s utterance to identify each word by meaning, position, conjugation, capitalization, plurality, and other factors.
- Custom Machine Learning (ML): Kore.ai uses state-of-the-art NLP algorithms and model for machine learning.
With its two-fold approach, Kore.ai Bots Platform enables you to instantly build conversational bots that can respond to 70% of conversations – with no language training to get started. It automatically enables the NLP capabilities to all built-in and custom bots, and powers the way chatbots communicate, understand, and respond to a user request. Kore. ai team has developed our hybrid NLP strategy, without outside vendors’ services, which In addition to detecting and performing tasks (changes to a system of records), provides an ability to build FAQ bots that return static responses. The platform uses a Knowledge Graph-based model that provides the intelligence required to represent the importance of key domain terms and their relationships in identifying user’s intent (in this case the most appropriate question). Machine learning models append the Knowledge graph to further arrive at the right Knowledge query. Once all the engines return scores and recommendations, Kore.ai has a ‘Ranking and Resolver’ engine that determines the winning intent based on the user utterance.
Advantages of Kore.ai Approach
Most products only use Machine Learning (ML) for natural language processing. The drawback of only using machine learning to train bots is that it takes a lot of data. With ML you must provide a collection of sentences(utterances) that match a chatbot’s intended goal (and eventually a group of sentences that do not). In this instance, the bot itself does not inherently understand an input sentence. Instead, it tries to measure how similar the data input is to what it already knows. An ML only approach can also be inaccurate because it requires extensive training of a bot for high success rates. Our approach combines Fundamental Meaning (FM) with Machine Learning(ML) to make it easy to build Natural language capable chatbots – whether or not rich training data is available. Together, enterprise developers can solve real-world dynamics and gain the inherent benefits of both approaches, while eliminating the shortcomings of the individual methods.
This section provides a comprehensive understanding of NLP using the following topics: