Natural Language

Advanced NLP Configurations

You can fine-tune intent detection for each languages enabled for your bot. To perform this action, follow the below steps: On the left pane, click Natural Language > Training > Thresholds & Configurations. Under the Thresholds & Configurations section, you can perform by customizing The Fundamental Meaning model – see here for…

Model Validation

Once you have built your virtual assistant and trained it, the Kore.ai platform builds an ML model mapping user utterance with bot intents (click here for more info). Once created, it is recommended to validate the model to understand and estimate an unbiased generalization performance of the ML model. Kore.ai…

Traits

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…

Ranking and Resolver

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…

Knowledge Graph Training

Training your Bot is not restricted to Machine Learning and Fundamental Meaning engines. You must 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 train…

Fundamental Meaning

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. In this section, we will discuss in detail the following topics to improve the FM engine: Manage…

Improving Bot Performance – NLP Optimization

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.…

NLP Settings and Guidelines

Intent Naming Guidelines Follow the below guidelines when naming your task (intent identifier): Use an 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…

Machine Learning

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…