manual extraction

简明释义

人工选录

英英释义

例句

1.In some cases, manual extraction 手动提取 is necessary when automated tools fail to capture all the data.

在某些情况下，当自动工具无法捕获所有数据时，需要进行手动提取 手动提取。

2.For the project, we had to rely on manual extraction 手动提取 of data from old records.

为了这个项目，我们不得不依赖于从旧记录中进行手动提取 手动提取 数据。

3.Due to the complexity of the database, we opted for manual extraction 手动提取 of the required records.

由于数据库的复杂性，我们选择了手动提取 手动提取 所需记录。

4.The researchers conducted manual extraction 手动提取 of specific variables from the study results.

研究人员对研究结果中的特定变量进行了手动提取 手动提取。

5.The data team performed manual extraction 手动提取 of customer feedback from the surveys.

数据团队从调查中进行了手动提取 手动提取 客户反馈。

作文

In today's rapidly advancing technological landscape, the term manual extraction refers to the process of retrieving specific data or information from a source without the aid of automated tools or software. This method is often employed in various fields, including research, data analysis, and even in everyday tasks such as organizing personal files. While automation has significantly improved efficiency in many areas, there are still instances where manual extraction proves to be invaluable. For instance, in academic research, scholars frequently need to gather qualitative data from interviews, surveys, or literature reviews. In these cases, manual extraction allows researchers to sift through vast amounts of information, identifying key themes and insights that may not be easily captured by automated systems. The human ability to interpret context and nuance plays a crucial role in this process, highlighting the importance of manual extraction in understanding complex topics. Moreover, businesses often rely on manual extraction when dealing with customer feedback or market research. By manually analyzing customer comments and reviews, companies can gain deeper insights into consumer behavior and preferences. This hands-on approach enables them to tailor their products and services to better meet the needs of their target audience. In contrast, automated tools might overlook subtle yet significant details that could influence business decisions. In the realm of data management, manual extraction can also be essential when working with unstructured data. For example, organizations may have a wealth of information stored in emails, PDFs, or other formats that do not lend themselves to easy extraction. In such cases, professionals must manually review these documents to extract relevant data, ensuring that important information is not lost in the shuffle. This process, while time-consuming, is often necessary to maintain data integrity and accuracy. However, it is important to recognize the limitations of manual extraction. The process can be labor-intensive and prone to human error, especially when dealing with large volumes of data. Consequently, organizations must weigh the benefits of manual extraction against the potential for mistakes and inefficiencies. To mitigate these risks, many companies adopt a hybrid approach, combining both automated tools and manual extraction to achieve optimal results. In conclusion, while the world increasingly leans towards automation, the significance of manual extraction remains undiminished. Whether in academic research, business analysis, or data management, the ability to manually extract and interpret information is a vital skill. As we continue to navigate an ever-evolving technological landscape, finding the right balance between automation and manual processes will be crucial for success. By understanding and embracing the value of manual extraction, individuals and organizations can enhance their decision-making capabilities and drive meaningful outcomes.

在当今快速发展的技术环境中，术语手动提取指的是从源头中检索特定数据或信息的过程，而不依赖于自动化工具或软件。这种方法常常在多个领域中使用，包括研究、数据分析，甚至在日常任务中，如整理个人文件。尽管自动化在许多领域显著提高了效率，但仍然有一些情况下，手动提取被证明是无价的。例如，在学术研究中，学者们经常需要从访谈、调查或文献回顾中收集定性数据。在这些情况下，手动提取使研究人员能够筛选大量信息，识别出关键主题和见解，这些内容可能无法通过自动化系统轻易捕捉到。人类对上下文和细微差别的理解能力在这一过程中发挥着至关重要的作用，突显了手动提取在理解复杂主题方面的重要性。此外，企业在处理客户反馈或市场研究时，通常依赖于手动提取。通过手动分析客户评论和评价，公司可以深入了解消费者行为和偏好。这种亲身参与的方法使他们能够调整产品和服务，以更好地满足目标受众的需求。相比之下，自动化工具可能会忽视那些可能影响商业决策的微妙但重要的细节。在数据管理领域，手动提取在处理非结构化数据时也可能至关重要。例如，组织可能拥有大量存储在电子邮件、PDF或其他格式中的信息，这些信息不易提取。在这种情况下，专业人员必须手动审查这些文档以提取相关数据，确保重要信息不会在混乱中丢失。这个过程虽然耗时，但在维护数据完整性和准确性方面往往是必要的。然而，重要的是要认识到手动提取的局限性。这个过程可能劳动密集且容易出现人为错误，尤其是在处理大量数据时。因此，组织必须权衡手动提取的好处与潜在的错误和低效之间的关系。为了减轻这些风险，许多公司采用混合方法，结合自动化工具和手动提取以实现最佳结果。总之，尽管世界越来越倾向于自动化，但手动提取的重要性依然未减。无论是在学术研究、商业分析还是数据管理中，手动提取和解释信息的能力都是一项重要技能。随着我们继续在不断发展的技术环境中航行，找到自动化和手动过程之间的正确平衡将对成功至关重要。通过理解和接受手动提取的价值，个人和组织可以增强决策能力，推动有意义的成果。