![]() ![]() If two or more entities in a pattern are contextually related, patterns use entity roles to extract contextual information about entities. ![]() For simple entities to be utilized by your app, you need to add utterances or use list entities. While patterns use entities, a pattern does not help detect a machine-learning entity.ĭo not expect to see improved entity prediction if you collapse multiple utterances into a single pattern. The " pattern.any" entity is used to extract free-form entities. Patterns do not improve machine-learning entity detectionĪ pattern is primarily meant to help the prediction of intents and roles. Setting an intent for a template utterance in a pattern is not a guarantee of the intent prediction, but it is a strong signal. Patterns use a mix of prediction techniques. The correct intent is not the top score but too close to the top score.Create a pattern to help LUIS understand the importance of the word order. If an app has between 10 and 20 utterances with different lengths of sentence, different word order, and even different words (synonyms of "subordinate", "manage", "report"), LUIS may return a low confidence score. Consider an employee, Tom, with a manager named Alice, and a team of subordinates named: Michael, Rebecca, and Carl. Given an employee's name and relationship, LUIS returns the employees involved. Patterns solve low intent confidenceĬonsider a Human Resources app that reports on the organizational chart in relation to an employee. A pattern allows you to gain more accuracy for an intent without providing several more utterances. Patterns are designed to improve accuracy when multiple utterances are very similar. We recommend migrating your LUIS applications to conversational language understanding to benefit from continued product support and multilingual capabilities. LUIS will be retired on October 1st 2025 and starting April 1st 2023 you will not be able to create new LUIS resources. ![]()
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