GRK 1994: Adaptive Informationsaufbereitung aus heterogenen Quellen

GRK 1994：来自异构源的自适应信息处理

• 批准号: 240177810
• 金额: --
• 依托单位: Technische Universität Darmstadt
• 依托单位国家: 德国
• 项目类别: Research Training Groups
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/240177810?language=en
• 关键词: GRK; 1994; Adaptive; Informationsaufbereitung; aus

The importance of thorough research under tight deadlines is increasing rapidly and the consequences of the quality of the research results are far-reaching, especially for decision making processes. At the same time, the amount of information is growing exponentially and there is a continual increase of complexity, heterogeneity, and a high variation in the quality of electronic information sources.The vision of this graduate program is to extract structured knowledge from heterogeneous sources using automated means in order to create dossiers of stylistically homogeneous content. Within this project, we develop methods that are able to adapt to different genres and domains, so that the results can easily be transferred to other tasks and – in later phases of the project – to other user groups and languages. The first phase of the graduate program focuses on multi-document summarization (MDS) as a prototypical task. As a representative use-case, we choose German documents on educational topics extracted from heavily heterogeneous sources. The German Institute for International Educational Research (DIPF) in Frankfurt/Main provides a common German document collection and also formulates user requirements and supports large-scale evaluations at different stages of the graduate program. Adaptive information processing is a complex task which requires extensive research on integrated methods that incorporate knowledge of the involved scientific disciplines. The main research questions of the graduate program are (A) the computational linguistic modeling of discourse phenomena in heterogeneous text genres, (B) the development of language technologies for heterogeneous MDS, (C) the representation and analysis of text-induced structures, (D) and the criteria and mechanisms for selecting and assessing the quality of heterogeneous sources and resulting summaries in information management. By investigating these questions in a densely connected research plan, the four research areas will jointly address our guiding research topic in breadth and depth.The qualification concept includes a collaboration across disciplines and locations, intensive international networking, scientific consultation of at least two PhD advisors and of one international co-advisor for each doctoral project, and the responsible participation of excellent post-doctoral researchers in doctoral supervision and training jointly with experienced advisors. The graduate program will form a central location for the qualification of young researchers in this highly demanded academic field in the Rhine-Main-Neckar region.

在紧迫的期限内进行彻底研究的重要性正在迅速增加，研究结果的质量影响深远，特别是对决策过程。与此同时，信息量呈指数级增长，电子信息源的复杂性、异质性和质量变化不断增加。该研究生课程的愿景是使用自动化手段从异构源中提取结构化知识，以创建风格同质内容的档案。在这个项目中，我们开发了能够适应不同类型和领域的方法，以便结果可以很容易地转移到其他任务中，并在项目的后期阶段转移到其他用户组和语言中。研究生课程的第一阶段侧重于多文档摘要（MDS）作为一个原型任务。作为一个有代表性的用例，我们选择了从高度异质性的来源中提取的关于教育主题的德语文档。位于法兰克福/美因河畔的德国国际教育研究所（DIPF）提供了一个通用的德语文档集，并制定了用户要求，并支持在研究生课程的不同阶段进行大规模评估。自适应信息处理是一项复杂的任务，需要广泛的研究综合方法，将知识的科学学科。研究生课程的主要研究问题是（A）在异构文本类型的话语现象的计算语言学建模，（B）异构MDS语言技术的发展，（C）文本诱导结构的表示和分析，（D）以及选择和评估异构源的质量的标准和机制，并在信息管理中产生摘要。通过在紧密联系的研究计划中调查这些问题，四个研究领域将共同解决我们在广度和深度上的指导研究课题。资格概念包括跨学科和地点的合作，密集的国际网络，至少两名博士顾问的科学咨询和每个博士项目的一名国际共同顾问，优秀的博士后研究人员负责任地参与博士生导师的指导和培养。该研究生课程将成为莱茵-美因-内卡地区这一高度要求的学术领域的年轻研究人员资格的中心位置。

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• 期刊: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
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A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning

• DOI: 10.18653/v1/s18-2027
• 发表时间: 2018-04
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Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers

• DOI: 10.18653/v1/2020.deelio-1.5
• 发表时间: 2020-05
• 期刊: ArXiv
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