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ScienceQA: a novel resource for question answering on scholarly articles. ScienceQA:一个新颖的学术文章问答资源。
IF 1.5 Q1 Social Sciences Pub Date : 2022-01-01 Epub Date: 2022-07-20 DOI: 10.1007/s00799-022-00329-y
Tanik Saikh, Tirthankar Ghosal, Amish Mittal, Asif Ekbal, Pushpak Bhattacharyya

Machine Reading Comprehension (MRC) of a document is a challenging problem that requires discourse-level understanding. Information extraction from scholarly articles nowadays is a critical use case for researchers to understand the underlying research quickly and move forward, especially in this age of infodemic. MRC on research articles can also provide helpful information to the reviewers and editors. However, the main bottleneck in building such models is the availability of human-annotated data. In this paper, firstly, we introduce a dataset to facilitate question answering (QA) on scientific articles. We prepare the dataset in a semi-automated fashion having more than 100k human-annotated context-question-answer triples. Secondly, we implement one baseline QA model based on Bidirectional Encoder Representations from Transformers (BERT). Additionally, we implement two models: the first one is based on Science BERT (SciBERT), and the second is the combination of SciBERT and Bi-Directional Attention Flow (Bi-DAF). The best model (i.e., SciBERT) obtains an F1 score of 75.46%. Our dataset is novel, and our work opens up a new avenue for scholarly document processing research by providing a benchmark QA dataset and standard baseline. We make our dataset and codes available here at https://github.com/TanikSaikh/Scientific-Question-Answering.

文档的机器阅读理解(MRC)是一个具有挑战性的问题,需要语篇级的理解。从学术文章中提取信息是研究人员快速理解基础研究并向前发展的关键用例,特别是在这个信息大流行的时代。研究文章的MRC也可以为审稿人和编辑提供有用的信息。然而,构建此类模型的主要瓶颈是人工注释数据的可用性。在本文中,我们首先引入一个数据集来促进科学文章的问答(QA)。我们以半自动的方式准备数据集,拥有超过10万个人工注释的上下文-问题-答案三元组。其次,我们实现了一个基于变形金刚双向编码器表示(BERT)的基线QA模型。此外,我们还实现了两个模型:第一个是基于科学BERT (SciBERT)的模型,第二个是SciBERT和双向注意流(Bi-DAF)的结合模型。最佳模型SciBERT的F1得分为75.46%。我们的数据集是新颖的,我们的工作通过提供基准QA数据集和标准基线,为学术文档处理研究开辟了一条新的途径。我们在https://github.com/TanikSaikh/Scientific-Question-Answering上提供了我们的数据集和代码。
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引用次数: 10
Mapping audiovisual content providers and resources in Greece. 映射希腊视听内容提供商和资源。
IF 1.5 Q1 Social Sciences Pub Date : 2022-01-01 Epub Date: 2022-02-03 DOI: 10.1007/s00799-022-00321-6
Afrodite Malliari, Ilias Nitsos, Sofia Zapounidou, Stavros Doropoulos

In Greece, there are many audiovisual resources available on the Internet that interest scientists and the general public. Although freely available, finding such resources often becomes a challenging task, because they are hosted on scattered websites and in different types/formats. These websites usually offer limited search options; at the same time, there is no aggregation service for audiovisual resources, nor a national registry for such content. To meet this need, the Open AudioVisual Archives project was launched and the first step in its development is to create a dataset with open access audiovisual material. The current research creates such a dataset by applying specific selection criteria in terms of copyright and content, form/use and process/technical characteristics. The results reported in this paper show that libraries, archives, museums, universities, mass media organizations, governmental and non-governmental organizations are the main types of providers, but the vast majority of resources are open courses offered by universities under the "Creative Commons" license. Providers have significant differences in terms of their collection management capabilities. Most of them do not own any kind of publishing infrastructure and use commercial streaming services, such as YouTube. In terms of metadata policy, most of the providers use application profiles instead of international metadata schemas.

在希腊,互联网上有许多让科学家和公众感兴趣的视听资源。虽然这些资源是免费的,但找到它们往往是一项具有挑战性的任务,因为它们托管在分散的网站上,以不同的类型/格式。这些网站通常提供有限的搜索选项;同时,音像资源没有聚合服务,也没有全国性的音像资源注册。为了满足这一需求,开放视听档案项目启动了,其发展的第一步是创建一个开放获取视听资料的数据集。目前的研究通过在版权和内容、形式/使用和过程/技术特征方面应用特定的选择标准来创建这样一个数据集。本文报告的结果表明,图书馆、档案馆、博物馆、大学、大众媒体组织、政府和非政府组织是主要的提供者类型,但绝大多数资源是大学根据“知识共享”许可提供的公开课程。提供者在集合管理功能方面存在显著差异。他们中的大多数人没有任何出版基础设施,而是使用商业流媒体服务,比如YouTube。就元数据策略而言,大多数提供者使用应用程序概要文件而不是国际元数据模式。
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引用次数: 2
CNN-based framework for classifying temporal relations with question encoder. 基于cnn的时间关系问题编码器分类框架。
IF 1.5 Q1 Social Sciences Pub Date : 2022-01-01 Epub Date: 2021-10-13 DOI: 10.1007/s00799-021-00310-1
Yohei Seki, Kangkang Zhao, Masaki Oguni, Kazunari Sugiyama

Temporal-relation classification plays an important role in the field of natural language processing. Various deep learning-based classifiers, which can generate better models using sentence embedding, have been proposed to address this challenging task. These approaches, however, do not work well due to the lack of task-related information. To overcome this problem, we propose a novel framework that incorporates prior information by employing awareness of events and time expressions (time-event entities) with various window sizes to focus on context words around the entities as a filter. We refer to this module as "question encoder." In our approach, this kind of prior information can extract task-related information from simple sentence embedding. Our experimental results on a publicly available Timebank-Dense corpus demonstrate that our approach outperforms some state-of-the-art techniques, including CNN-, LSTM-, and BERT-based temporal relation classifiers.

时间关系分类在自然语言处理领域中占有重要地位。为了解决这一具有挑战性的任务,已经提出了各种基于深度学习的分类器,这些分类器可以使用句子嵌入生成更好的模型。然而,由于缺乏与任务相关的信息,这些方法不能很好地工作。为了克服这个问题,我们提出了一个新的框架,该框架通过使用具有不同窗口大小的事件和时间表达式(时间-事件实体)的感知来融合先验信息,以关注实体周围的上下文词作为过滤器。我们把这个模块称为“问题编码器”。在我们的方法中,这种先验信息可以从简单句嵌入中提取任务相关信息。我们在公开可用的时间银行密集语料库上的实验结果表明,我们的方法优于一些最先进的技术,包括基于CNN、LSTM和bert的时间关系分类器。
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引用次数: 1
Scientific paper recommendation systems: a literature review of recent publications. 科学论文推荐系统:最近发表的文献综述。
IF 1.5 Q1 Social Sciences Pub Date : 2022-01-01 Epub Date: 2022-10-05 DOI: 10.1007/s00799-022-00339-w
Christin Katharina Kreutz, Ralf Schenkel

Scientific writing builds upon already published papers. Manual identification of publications to read, cite or consider as related papers relies on a researcher's ability to identify fitting keywords or initial papers from which a literature search can be started. The rapidly increasing amount of papers has called for automatic measures to find the desired relevant publications, so-called paper recommendation systems. As the number of publications increases so does the amount of paper recommendation systems. Former literature reviews focused on discussing the general landscape of approaches throughout the years and highlight the main directions. We refrain from this perspective, instead we only consider a comparatively small time frame but analyse it fully. In this literature review we discuss used methods, datasets, evaluations and open challenges encountered in all works first released between January 2019 and October 2021. The goal of this survey is to provide a comprehensive and complete overview of current paper recommendation systems.

科学写作以已经发表的论文为基础。人工识别出版物阅读,引用或考虑作为相关论文依赖于研究人员的能力,以确定合适的关键字或最初的论文,从文献检索可以开始。论文数量的迅速增加要求采用自动措施来寻找所需的相关出版物,即所谓的论文推荐系统。随着出版物数量的增加,论文推荐系统的数量也在增加。以前的文献综述侧重于讨论多年来方法的总体概况,并突出了主要方向。我们避免这种观点,相反,我们只考虑一个相对较小的时间框架,但全面分析它。在这篇文献综述中,我们讨论了在2019年1月至2021年10月期间首次发布的所有作品中使用的方法、数据集、评估和遇到的公开挑战。本调查的目的是提供当前论文推荐系统的全面和完整的概述。
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引用次数: 12
Children’s query formulation and search result exploration 儿童查询公式和搜索结果探索
IF 1.5 Q1 Social Sciences Pub Date : 2021-11-30 DOI: 10.1007/s00799-021-00316-9
Nicholas Vanderschantz, A. Hinze
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引用次数: 1
MELHISSA: a multilingual entity linking architecture for historical press articles MELHISSA:历史新闻文章的多语言实体链接架构
IF 1.5 Q1 Social Sciences Pub Date : 2021-11-29 DOI: 10.1007/s00799-021-00319-6
Elvys Linhares Pontes, Luis Adrián Cabrera-Diego, José G. Moreno, Emanuela Boros, Ahmed Hamdi, Antoine Doucet, Nicolas Sidère, Mickaël Coustaty
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引用次数: 4
SchenQL: in-depth analysis of a query language for bibliographic metadata SchenQL:深入分析书目元数据的查询语言
IF 1.5 Q1 Social Sciences Pub Date : 2021-11-23 DOI: 10.1007/s00799-021-00317-8
Christin Katharina Kreutz, Michael Wolz, Jascha Knack, B. Weyers, Ralf Schenkel
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引用次数: 4
VeTo+: improved expert set expansion in academia VeTo+:改进学术界的专家集扩展
IF 1.5 Q1 Social Sciences Pub Date : 2021-11-15 DOI: 10.1007/s00799-021-00318-7
Serafeim Chatzopoulos, Thanasis Vergoulis, Theodore Dalamagas, Christos Tryfonopoulos
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引用次数: 1
Cross-lingual citations in English papers: a large-scale analysis of prevalence, usage, and impact 英语论文中的跨语言引用:流行、使用和影响的大规模分析
IF 1.5 Q1 Social Sciences Pub Date : 2021-11-07 DOI: 10.1007/s00799-021-00312-z
T. Saier, Michael Färber, Tornike Tsereteli
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引用次数: 5
Correspondence as the primary measure of information quality for web archives: a human-centered grounded theory study 通信作为网络档案信息质量的主要衡量标准:以人为本的扎根理论研究
IF 1.5 Q1 Social Sciences Pub Date : 2021-11-03 DOI: 10.1007/s00799-021-00314-x
Brenda Reyes Ayala
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引用次数: 3
期刊
International Journal on Digital Libraries
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