Natural Language

In natural conversations, it is very common that a user provides background/relevant information while describing a specific scenario. Traits are specific entities, attributes, or details that the users express in their conversations. The utterance may not directly convey any specific intent, but the traits present in the utterance are used…

The Kore.ai NLP engine uses Machine Learning, Fundamental Meaning, and Knowledge Graph (if any) models to match intents. All the three Kore.ai engines finally deliver their findings to the Kore.ai Ranking and Resolver component as either exact matches or probable matches. Ranking and Resolver determines the final winner of the…

Fundamental Meaning is a computational linguistics approach that is 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. FM Engine Overview The Fundamental Meaning model is a deterministic model that uses semantic rules and…

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.…

This article provides you with some essential guidelines to help you optimize your workflow with the XO Platform’s NLP, and thus improve your VA’s performance. For this purpose, we also recommend that you read NLP Overview and Optimizing NLP. Intent Naming Guidelines Follow the below guidelines when naming your task…