automatic indexing

简明释义

1. 自动编索引; 2. 自动变址; 3. 自动分度;

英英释义

例句

1.The library uses automatic indexing to organize books efficiently.

这个图书馆使用自动索引来高效地组织书籍。

2.The new database system includes automatic indexing to improve query performance.

新的数据库系统包括自动索引以提高查询性能。

3.The software features automatic indexing that saves time for data management.

该软件具有自动索引功能，可以为数据管理节省时间。

4.Many search engines rely on automatic indexing to provide accurate search results.

许多搜索引擎依赖于自动索引来提供准确的搜索结果。

5.With automatic indexing, researchers can quickly find relevant articles in databases.

通过自动索引，研究人员可以快速在数据库中找到相关的文章。

作文

In the digital age, the volume of information available to us is unprecedented. As we navigate through vast amounts of data, the need for efficient retrieval methods has become increasingly important. One such method that has gained prominence is automatic indexing, which refers to the process of automatically assigning index terms to documents based on their content. This technique not only streamlines the organization of information but also enhances the accessibility of resources for users. The concept of automatic indexing is rooted in information retrieval systems, where algorithms analyze text to identify key concepts and keywords. These systems utilize natural language processing (NLP) techniques to understand the context and semantics of the content. By doing so, they can generate relevant index terms that accurately reflect the subject matter of the documents. This is particularly beneficial in environments where large datasets are common, such as academic research, libraries, and corporate databases.One significant advantage of automatic indexing is its ability to save time and reduce human error. Traditionally, indexing was a manual process that required extensive knowledge and attention to detail. Indexers would read through documents and assign keywords based on their understanding, which could lead to inconsistencies and omissions. With automatic indexing, these tasks are performed by algorithms that can process information at a much faster rate and with greater accuracy. This efficiency allows organizations to manage their information more effectively, ensuring that users can find what they need without sifting through irrelevant material.Moreover, automatic indexing supports the discovery of hidden relationships within data. By analyzing patterns and connections among various documents, these systems can uncover insights that may not be immediately apparent. For instance, in academic research, automatic indexing can help researchers identify trends in literature, discover new areas of study, or even find collaborators working on similar topics. This capability fosters innovation and encourages the sharing of knowledge across disciplines.However, it is essential to acknowledge the limitations of automatic indexing. While algorithms have advanced significantly, they still struggle with nuances in language, such as idioms, sarcasm, and cultural references. This can result in misinterpretations or the omission of critical context that a human indexer might catch. Therefore, many organizations choose to combine automatic indexing with human oversight to ensure the highest quality of indexing. This hybrid approach leverages the speed and efficiency of automated systems while maintaining the accuracy and contextual understanding provided by human expertise.In conclusion, automatic indexing represents a transformative approach to information management in our increasingly data-driven world. By automating the indexing process, organizations can enhance the efficiency and accuracy of their information retrieval systems. While challenges remain, the benefits of automatic indexing—including time savings, improved access to information, and the ability to uncover hidden insights—make it a valuable tool in the quest for effective knowledge management. As technology continues to evolve, we can expect automatic indexing to play an even more significant role in how we interact with information in the future.

在数字时代，我们可获得的信息量前所未有。当我们在大量数据中导航时，效率检索方法的需求变得越来越重要。其中一种获得突出的方法是自动索引，指的是根据文档内容自动分配索引术语的过程。这种技术不仅简化了信息的组织，还增强了用户对资源的可访问性。自动索引的概念根植于信息检索系统，其中算法分析文本以识别关键概念和关键词。这些系统利用自然语言处理（NLP）技术来理解内容的上下文和语义。通过这样做，它们可以生成准确反映文档主题的相关索引术语。这在大型数据集普遍存在的环境中尤其有益，例如学术研究、图书馆和企业数据库。自动索引的一个显著优势是能够节省时间并减少人为错误。传统上，索引是一个人工过程，需要广泛的知识和细致的关注。索引者会阅读文档并根据自己的理解分配关键词，这可能导致不一致和遗漏。随着自动索引的出现，这些任务由算法执行，可以更快地处理信息，并且准确性更高。这种效率使组织能够更有效地管理信息，确保用户能够找到所需内容，而无需筛选无关材料。此外，自动索引支持数据中隐藏关系的发现。通过分析各种文档之间的模式和联系，这些系统可以揭示可能并不明显的见解。例如，在学术研究中，自动索引可以帮助研究人员识别文献中的趋势、发现新的研究领域，甚至找到在类似主题上工作的合作者。这种能力促进了创新，并鼓励跨学科的知识分享。然而，必须承认自动索引的局限性。虽然算法已经取得了显著进展，但它们仍然在语言的细微差别方面存在困难，例如习语、讽刺和文化参考。这可能导致误解或遗漏人类索引者可能捕捉到的关键上下文。因此，许多组织选择将自动索引与人工监督相结合，以确保索引的最高质量。这种混合方法利用了自动化系统的速度和效率，同时保持了人类专业知识提供的准确性和上下文理解。总之，自动索引代表了一种在我们日益数据驱动的世界中对信息管理的变革性方法。通过自动化索引过程，组织可以提高其信息检索系统的效率和准确性。尽管仍然存在挑战，但自动索引的好处——包括节省时间、改善信息访问和发现隐藏见解的能力——使其成为有效知识管理的宝贵工具。随着技术的不断发展，我们可以期待自动索引在未来如何与信息交互时扮演更重要的角色。