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Named-entity recognition. The main task of the script is to extract named entities, such as the name of the manager, company, greetings and goodbyes. The dataset consists of Russian-language replicas between managers and clients. The Spacy library was chosen as the main tool.

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NER

Задачи, которые выполняет скрипт:

  • Извлекает реплики с приветствием – где менеджер поздоровался.
  • Извлекает реплики, где менеджер представил себя.
  • Извлекает имя менеджера.
  • Извлекает название компании.
  • Извлекает реплики, где менеджер попрощался.
  • Провеяет требования к менеджеру: «В каждом диалоге обязательно необходимо поздороваться и попрощаться с клиентом

Tasks that the script performs:

  • Extracts cues with a greeting - where the manager said hello.
  • Retrieves cues where the manager introduced himself.
  • Retrieves the name of the manager.
  • Retrieves the company name.
  • Extracts lines where the manager said goodbye.
  • It checks the requirements for the manager: "In each dialogue, it is necessary to say hello and say goodbye to the client"

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Named-entity recognition. The main task of the script is to extract named entities, such as the name of the manager, company, greetings and goodbyes. The dataset consists of Russian-language replicas between managers and clients. The Spacy library was chosen as the main tool.

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