Natural Language

The Machine Learning Model (ML Model) graph presents an at-a-glance view of the performance of your trained utterances against the bot tasks. The ML Model graph evaluates all the training utterances against each bot task and plots them into one of these quadrants of the task: True Positive (True +ve),…

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…

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…

Training your Bot is not restricted to Machine Learning and Fundamental Meaning engines. You need to train the Knowledge Graph engine, too. Knowledge Graph engine responds to users’ intents by identifying the appropriate questions from the Knowledge Graph. Knowledge Graph From the Knowledge Graph, follow these steps to build and…

Fundamental Meaning is 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. In this section we will discuss in details the following topics to improve the FM engine: Managing Synonyms…

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.…

Intent Naming Guidelines Follow the below guidelines when naming your task (intent identifier) Use action verb, an object and possibly a modifier (placed before or after the object). Typically, an intent name consists of 2 to 4 words. Use less than 5 words to convey the purpose of the task…

Developers need to provide sample utterances for each intent (task) the bot needs to identify to train the machine learning model. The platform ML engine will build a model that will try to map a user utterance to one of the bot intents. Kore.ai’s Bots Platform allows fully unsupervised machine…