美利体育登录入口官网:DIN DKE SPEC 99001:2022-05
定义用于训练人工智能的数据成功方法 应用重点：问答 文本
Defining Data Success Methods for Training Artificial Intelligence Application Focus: Question and Answer Text
- 标准号
- DIN DKE SPEC 99001:2022-05
- 发布
- 2022年
- 总页数
- 21页
- 发布单位
- 德国标准化学会
- 当前最新
- DIN DKE SPEC 99001:2022-05
- 本体
- 机器学习 自然语言处理
- 适用范围
- This document gives guidelines for the labelling of training data for QA systems and specifies the characteristics of labels. Furthermore, it defines terms related to NLP and labelling. The guidelines presented in this document cover requirements related to the labelling process, onboarding, tooling and ergonomics, as well as QCA for open-domain QA. This document is applicable to all industries, topics, languages, document types and use cases. Labelling is used to tailor NLP models to specific domains. There is no limitation of applicability with respect to the underlying technical basics. This document applies to all language models and model parameters. This document focuses on open-domain (text-based) QA and does not cover QA for knowledge graphs or relational databases.
| 术语 | 描述 |
|---|---|
| 人工智能 artificial intelligence | capability of a functional unit to perform functions that are generally associated with human intelligence such as reasoning and learning |
| 自然语言处理 Natural Language Processing | field covering knowledge and techniques involved in the processing of linguistic data by a computer |
| 问题回答系统 Question-Answering system | field in computer science focusing on systems that can answer questions posed by humans in natural language in an automated manner |
| 训练数据 Training Data | data used as an input to an algorithm for development and training of machine learning models |
| 标签 label |