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English Conversation Chat Dataset for Healthcare Domain
- futurebeeai.com
wavUpdated Aug 1, 2022
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EmailClick to copy linkLink copiedCiteFutureBee AI (2022). English Conversation Chat Dataset for Healthcare Domain [Dataset]. https://www.futurebeeai.com/dataset/text-dataset/english-healthcare-domain-conversation-text-datasetwavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Dataset funded byFutureBeeAIDescriptionIntroduction
The dataset comprises over 12,000 chat conversations, each focusing on specific Healthcare related topics. Each conversation provides a detailed interaction between a call center agent and a customer, capturing real-life scenarios and language nuances.
•Participants Details: 200+ native English participants from the FutureBeeAI community.•Word Count & Length: Chats are diverse, averaging 300 to 700 words and 50 to 150 turns across both speakers.Topic Diversity
The chat dataset covers a wide range of conversations on Healthcare topics, ensuring that the dataset is comprehensive and relevant for training and fine-tuning models for various Healthcare use cases. It offers diversity in terms of conversation topics, chat types, and outcomes, including both inbound and outbound chats with positive, neutral, and negative outcomes.
•Inbound Chats:•Appointment Scheduling•New Patient Registration•Surgery Consultation•Consultation regarding Diet, and many more•Outbound Chats:•Appointment Reminder•Health & Wellness Subscription Programs•Lab Test Results•Health Risk Assessments•Preventive Care Reminders, and many moreLanguage Variety & Nuances
The conversations in this dataset capture the diverse language styles and expressions prevalent in English Healthcare interactions. This diversity ensures the dataset accurately represents the language used by English speakers in Healthcare contexts.
The dataset encompasses a wide array of language elements, including:
•Naming Conventions: Chats include a variety of English personal and business names.•Localized Details: Real-world addresses, emails, phone numbers, and other contact information as according to different English-speaking regions.•Temporal and Numeric Expressions: Dates, times, currencies, and numbers in English forms, adhering to local conventions.•Idiomatic Expressions and Slang: It includes local slang, idioms, and informal phrase present in English Healthcare conversations.This linguistic authenticity ensures that the dataset equips researchers and developers with a comprehensive understanding of the intricate language patterns, cultural references, and communication styles inherent to English Healthcare interactions.
Conversational Flow and Interaction Types
The dataset includes a broad range of conversations, from simple inquiries to detailed discussions, capturing the dynamic nature of Healthcare customer-agent interactions.
•Simple Inquiries•Detailed Discussions•Transactional Interactions•Problem-Solving Dialogues•Advisory Sessions•Routine Checks and Follow-UpsEach of these conversations contains various aspects of conversation flow like:
•Greetings•Authentication•Information gathering•Resolution identification•Solution Delivery•Closing and Follow-ups•Feedback, etcThis structured and varied conversational flow enables the creation of advanced NLP models that can effectively manage and respond to a wide range of customer service scenarios.
Data Format and Structure
The dataset is available in JSON, CSV, and TXT formats, with each conversation containing attributes like participant identifiers and chat
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Hindi Conversation Chat Dataset for Healthcare Domain
- futurebeeai.com
wavUpdated Aug 1, 2022ShareFacebookTwitterEmailClick to copy linkLink copiedCiteFutureBee AI (2022). Hindi Conversation Chat Dataset for Healthcare Domain [Dataset]. https://www.futurebeeai.com/dataset/text-dataset/hindi-healthcare-domain-conversation-text-datasetwavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Dataset funded byFutureBeeAIDescriptionIntroduction
The dataset comprises over 12,000 chat conversations, each focusing on specific Healthcare related topics. Each conversation provides a detailed interaction between a call center agent and a customer, capturing real-life scenarios and language nuances.
•Participants Details: 200+ native Hindi participants from the FutureBeeAI community.•Word Count & Length: Chats are diverse, averaging 300 to 700 words and 50 to 150 turns across both speakers.Topic Diversity
The chat dataset covers a wide range of conversations on Healthcare topics, ensuring that the dataset is comprehensive and relevant for training and fine-tuning models for various Healthcare use cases. It offers diversity in terms of conversation topics, chat types, and outcomes, including both inbound and outbound chats with positive, neutral, and negative outcomes.
•Inbound Chats:•Appointment Scheduling•New Patient Registration•Surgery Consultation•Consultation regarding Diet, and many more•Outbound Chats:•Appointment Reminder•Health & Wellness Subscription Programs•Lab Test Results•Health Risk Assessments•Preventive Care Reminders, and many moreLanguage Variety & Nuances
The conversations in this dataset capture the diverse language styles and expressions prevalent in Hindi Healthcare interactions. This diversity ensures the dataset accurately represents the language used by Hindi speakers in Healthcare contexts.
The dataset encompasses a wide array of language elements, including:
•Naming Conventions: Chats include a variety of Hindi personal and business names.•Localized Details: Real-world addresses, emails, phone numbers, and other contact information as according to different Hindi-speaking regions.•Temporal and Numeric Expressions: Dates, times, currencies, and numbers in Hindi forms, adhering to local conventions.•Idiomatic Expressions and Slang: It includes local slang, idioms, and informal phrase present in Hindi Healthcare conversations.This linguistic authenticity ensures that the dataset equips researchers and developers with a comprehensive understanding of the intricate language patterns, cultural references, and communication styles inherent to Hindi Healthcare interactions.
Conversational Flow and Interaction Types
The dataset includes a broad range of conversations, from simple inquiries to detailed discussions, capturing the dynamic nature of Healthcare customer-agent interactions.
•Simple Inquiries•Detailed Discussions•Transactional Interactions•Problem-Solving Dialogues•Advisory Sessions•Routine Checks and Follow-UpsEach of these conversations contains various aspects of conversation flow like:
•Greetings•Authentication•Information gathering•Resolution identification•Solution Delivery•Closing and Follow-ups•Feedback, etcThis structured and varied conversational flow enables the creation of advanced NLP models that can effectively manage and respond to a wide range of customer service scenarios.
Data Format and Structure
The dataset is available in JSON, CSV, and TXT formats, with each conversation containing attributes like participant identifiers and chat messages, designed to
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Bengali Conversation Chat Dataset for Healthcare Domain
- futurebeeai.com
wavUpdated Aug 1, 2022ShareFacebookTwitterEmailClick to copy linkLink copiedCiteFutureBee AI (2022). Bengali Conversation Chat Dataset for Healthcare Domain [Dataset]. https://www.futurebeeai.com/dataset/text-dataset/bengali-healthcare-domain-conversation-text-datasetwavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Dataset funded byFutureBeeAIDescriptionIntroduction
The dataset comprises over 12,000 chat conversations, each focusing on specific Healthcare related topics. Each conversation provides a detailed interaction between a call center agent and a customer, capturing real-life scenarios and language nuances.
•Participants Details: 200+ native Bengali participants from the FutureBeeAI community.•Word Count & Length: Chats are diverse, averaging 300 to 700 words and 50 to 150 turns across both speakers.Topic Diversity
The chat dataset covers a wide range of conversations on Healthcare topics, ensuring that the dataset is comprehensive and relevant for training and fine-tuning models for various Healthcare use cases. It offers diversity in terms of conversation topics, chat types, and outcomes, including both inbound and outbound chats with positive, neutral, and negative outcomes.
•Inbound Chats:•Appointment Scheduling•New Patient Registration•Surgery Consultation•Consultation regarding Diet, and many more•Outbound Chats:•Appointment Reminder•Health & Wellness Subscription Programs•Lab Test Results•Health Risk Assessments•Preventive Care Reminders, and many moreLanguage Variety & Nuances
The conversations in this dataset capture the diverse language styles and expressions prevalent in Bengali Healthcare interactions. This diversity ensures the dataset accurately represents the language used by Bengali speakers in Healthcare contexts.
The dataset encompasses a wide array of language elements, including:
•Naming Conventions: Chats include a variety of Bengali personal and business names.•Localized Details: Real-world addresses, emails, phone numbers, and other contact information as according to different Bengali-speaking regions.•Temporal and Numeric Expressions: Dates, times, currencies, and numbers in Bengali forms, adhering to local conventions.•Idiomatic Expressions and Slang: It includes local slang, idioms, and informal phrase present in Bengali Healthcare conversations.This linguistic authenticity ensures that the dataset equips researchers and developers with a comprehensive understanding of the intricate language patterns, cultural references, and communication styles inherent to Bengali Healthcare interactions.
Conversational Flow and Interaction Types
The dataset includes a broad range of conversations, from simple inquiries to detailed discussions, capturing the dynamic nature of Healthcare customer-agent interactions.
•Simple Inquiries•Detailed Discussions•Transactional Interactions•Problem-Solving Dialogues•Advisory Sessions•Routine Checks and Follow-UpsEach of these conversations contains various aspects of conversation flow like:
•Greetings•Authentication•Information gathering•Resolution identification•Solution Delivery•Closing and Follow-ups•Feedback, etcThis structured and varied conversational flow enables the creation of advanced NLP models that can effectively manage and respond to a wide range of customer service scenarios.
Data Format and Structure
The dataset is available in JSON, CSV, and TXT formats, with each conversation containing attributes like participant identifiers and chat
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Introduction
The dataset comprises over 12,000 chat conversations, each focusing on specific Healthcare related topics. Each conversation provides a detailed interaction between a call center agent and a customer, capturing real-life scenarios and language nuances.
Topic Diversity
The chat dataset covers a wide range of conversations on Healthcare topics, ensuring that the dataset is comprehensive and relevant for training and fine-tuning models for various Healthcare use cases. It offers diversity in terms of conversation topics, chat types, and outcomes, including both inbound and outbound chats with positive, neutral, and negative outcomes.
Language Variety & Nuances
The conversations in this dataset capture the diverse language styles and expressions prevalent in English Healthcare interactions. This diversity ensures the dataset accurately represents the language used by English speakers in Healthcare contexts.
The dataset encompasses a wide array of language elements, including:
This linguistic authenticity ensures that the dataset equips researchers and developers with a comprehensive understanding of the intricate language patterns, cultural references, and communication styles inherent to English Healthcare interactions.
Conversational Flow and Interaction Types
The dataset includes a broad range of conversations, from simple inquiries to detailed discussions, capturing the dynamic nature of Healthcare customer-agent interactions.
Each of these conversations contains various aspects of conversation flow like:
This structured and varied conversational flow enables the creation of advanced NLP models that can effectively manage and respond to a wide range of customer service scenarios.
Data Format and Structure
The dataset is available in JSON, CSV, and TXT formats, with each conversation containing attributes like participant identifiers and chat