submitted time 2024-04-03 Cooperative journals: 《农业图书情报学报》
Abstract： [Purpose/Significance] Deep learning for language modeling is one of the major methods and advanced technologies to enhance language intelligence of machines at present, which has become an indispensable important technical means for automatic processing and analysis of data resources, and intelligent mining of information and knowledge. However, there are still some difficulties in using deep learning for language modeling for technology development and application service in the library and information science (LIS) field. Therefore, this study systematically reviews and reveals the research progress, technical principles, and development methods of deep learning for language modeling, with the aim at providing reliable theoretical basis and feasible methodological paths for the deep understanding and application of deep learning for language modeling for librarians and fellow practitioners. [Method/Process] The data used in this study were collected from the WOS core database, CNKI literature database, arXiv preprint repository, GitHub open-source software hosting platform and the open resources on the Internet. Based on these data, this paper first systematically investigates the background, basic feature representation algorithms, and representative application development tools of deep learning for language modeling, reveals their dynamic evolution and technical principles, and analyzes the advantages and disadvantages and applicability of each algorithm model and development tool. Second, an in-depth analysis of the possible challenging problems faced by the development and application of deep learning for language modeling was performed, and two strategic approaches to expand their application capabilities were put forward. [Results/Conclusions] The important challenges faced by the application and development of deep learning for language modeling include numerous parameters and difficulties to adjust accuracy, relying on a large amount of accurate training data, difficulties in making changes, and the intellectual property and information security issues. In the future, we will start from two aspects of specific domains and feature engineering to expand and improve the application capabilities of deep learning for language modeling. Specifically, we focus on consideration of the collection and preparation of domain data, selection of model architecture, participation of domain experts, and optimization for specific tasks, in order to ensure that the data source of the model is more reliable and secure, and the application effect is more accurate and practical. Moreover, the strategic methods for feature engineering to expand the application capabilities of deep learning for language modeling include selecting appropriate features, feature pre-processing, feature selection, and feature dimensionality reduction. These strategies can help improve the performance and efficiency of deep learning for language models, making them more suitable for specific tasks or domains. To sum up, LIS institutions should leverage the deep learning for language modeling related technologies, guided by the needs of scientific research and social development, and based on advantages of existing literature data resources and knowledge services; they should carry out innovative professional or vertical domain intelligent knowledge management and application service, and develop technology and systems with independent intellectual property rights, which is their long-term sustainable development path.
Subjects: Library Science，Information Science >> Information Science submitted time 2017-12-05 Cooperative journals: 《数据分析与知识发现》
Abstract：【目的】实现对多源异构科技信息的长期监测、自动采集发布与存储管理, 以满足专题领域科技研究的需 求。【方法】结合 CSpace 的应用扩展需求, 设计开发了基于 CSpace 的可配置化的科技信息自动监测功能, 着重 研究和解决了多源异构科技信息采集内容规则的可配置化实现、与 CSpace 交互的自动采集发布接口的可配置化 实现等关键技术问题, 并以海洋科技信息的自动监测采集为例进行应用研究。【结果】能够实现对多源异构科技 信息的自动监测采集, 为科技平台建设提供良好支持。【局限】采集内容规则配置过程比较复杂; 不支持对一些 需要登录的复杂站点的自动监测。【结论】该功能方法较大程度上扩展了 CSpace 的数据采集集成功能, 且具有 一定的通用性、可配置性与松耦合性, 可应用于多个科技信息监测领域。
Subjects: Library Science，Information Science >> Information Science submitted time 2017-12-05 Cooperative journals: 《数据分析与知识发现》
Abstract：【目的】提出机构知识库影音支持能力扩展方向, 实现 CSpace 机构知识库影音支持能力扩展。【应用背景】 影音知识资源在机构产出中所占比例不断增长, 扩展机构知识库影音支持能力可更好地揭示、发现影音知识资 源, 挖掘和利用其学术研究价值和潜力。【方法】分析用户的应用需求和国内外机构知识库影音支持服务的发展 趋势, 构建机构知识库影音资源支持功能扩展框架, 选择其中的关键技术和方法搭建实验平台, 探索将其应用 于 CSpace 系统的可行性。【结果】实现了影音格式转换、视频场景分析和具有场景导航功能的播放器。【结论】 影音转码稳定性和效率较高, 其他影音支持功能离实用还存在一定距离, 将影音格式转换技术应用于 CSpace 机 构知识库系统中, 能够扩展机构知识库的影音支持服务。
Subjects: Library Science，Information Science >> Information Science submitted time 2017-11-30 Cooperative journals: 《数据分析与知识发现》
Abstract：【目的】实现对多源异构科技信息的长期监测、自动采集发布与存储管理, 以满足专题领域科技研究的需 求。【方法】结合 CSpace 的应用扩展需求, 设计开发了基于 CSpace 的可配置化的科技信息自动监测功能, 着重 研究和解决了多源异构科技信息采集内容规则的可配置化实现、与 CSpace 交互的自动采集发布接口的可配置化 实现等关键技术问题, 并以海洋科技信息的自动监测采集为例进行应用研究。【结果】能够实现对多源异构科技 信息的自动监测采集, 为科技平台建设提供良好支持。【局限】采集内容规则配置过程比较复杂; 不支持对一些 需要登录的复杂站点的自动监测。【结论】该功能方法较大程度上扩展了 CSpace 的数据采集集成功能, 且具有 一定的通用性、可配置性与松耦合性, 可应用于多个科技信息监测领域。
Subjects: Library Science，Information Science >> Information Science submitted time 2017-11-30 Cooperative journals: 《数据分析与知识发现》
Abstract：【目的】提出机构知识库影音支持能力扩展方向, 实现 CSpace 机构知识库影音支持能力扩展。【应用背景】 影音知识资源在机构产出中所占比例不断增长, 扩展机构知识库影音支持能力可更好地揭示、发现影音知识资 源, 挖掘和利用其学术研究价值和潜力。【方法】分析用户的应用需求和国内外机构知识库影音支持服务的发展 趋势, 构建机构知识库影音资源支持功能扩展框架, 选择其中的关键技术和方法搭建实验平台, 探索将其应用 于 CSpace 系统的可行性。【结果】实现了影音格式转换、视频场景分析和具有场景导航功能的播放器。【结论】 影音转码稳定性和效率较高, 其他影音支持功能离实用还存在一定距离, 将影音格式转换技术应用于 CSpace 机 构知识库系统中, 能够扩展机构知识库的影音支持服务。
Hits
Hits
Downloads
Comment