应用关键动词抽取生物医学实体间语义关系研究综述

对利用关键动词(即语义关系动词)进行关系抽取的研究进行回顾,并对生物医学语义关系抽取的常用方法进行详细介绍。认为目前关系抽取研究中所利用的关键动词主要是人工选取的,涉及的主观因素较多,影响关键动词选取的客观性、准确性和全面性。因此,如何自动化地找到这些动词可能会成为今后关系挖掘的一项关键任务。     

OAI-PMH元数据的关联数据化方法研究

OAI-PMH元数据的关联数据化是指将OAI仓储中的元数据转换为关联数据,可以解决用户在按传统方式采集这些元数据时所遇到的问题.总结现有相关研究的不足,描述OAI-PMH元数据的关联数据化方法的基本原理,对URI分配方法、用于描述关联数据的元数据元素的确定、关联信息的生成等关键问题进行探讨.     

Linking Spatial Named Entities to the Web of Data for Geographical Analysis of Historical Texts

In our work, we are interested in facilitating the exploration by scholars of the geography of texts: in particular, historical narrative texts describing routes. Semantic annotation constitutes the first step to enrich such text with the necessary information for producing analytical maps. The present article focuses on the disambiguation of spatial named entities (SNE) by the attribution of an identifier of the ever-growing Web of Data. This giant knowledge base (KB) provides qualitative spatial information about geographic entities, in particular spatial relations such as (:Paris :southOf :Lille), (:Paris :country :France). We thus propose a graph-matching algorithm relying on the A^* algorithm and graph-edit distances for choosing the best referent in the KB for the SNE. We performed preliminary experiments and noted the clear gain in performance. We propose some examples of maps that are built semi-automatically. Finally, we draw conclusions and describe our plans of future work.     

利用实体解析的跨社交媒体同一用户识别

[目的/意义] 跨领域关联实体一直是实体解析研究的主题,本文旨在不同的社交媒体（跨社交媒体）中找到属于同一用户的账户。[方法/过程] 在传统近似字符串匹配技术的基础上,提出使用属性值结合社交媒体中的链接和文本内容的方法,比较两个不同社交媒体账户的属性相似度、邻域相似度和关键词相似度这三个匹配函数,以此提高识别这两个账户是否是同一个人的精确度。并利用社交媒体Facebook和Twitter数据作为实验数据集,针对匹配函数的不同组合进行试验。[结果/结论] 结果表明,三个匹配函数的组合能够得到更多的账户匹配为同一用户,同时精确度也很高,达到0.923。本文提出的方法在Facebook和Twitter上的成功运用,给其他社交媒体平台或者其他领域的实体关联的研究提供了一条新的路径。     

谈思想道德修养课程的教学实践

高职院校思想道德修养课对于引导大学生树立正确的世界观、人生观、价值观，引导大学生健康成长发挥着重要作用．高职院校思想道德修养不仅依靠理论教育，而且要将理论与实践相结合，使学生直接参与到教学实践中进行自我教育、自我完善，从而做到“知”、“行”统一．     

The KBART’s Potential beyond OpenURL Linking

Knowledge Base and Related Tools (KBART), initially founded with a focus on improving OpenURL linking through the definition of best practices for exchanging title-level metadata, continues to gain traction and has the potential to be an initiative that improves not only link resolver quality but also improves discovery services, electronic resource management (ERM) systems, and even Counting Online Usage of Networked Electronic Resources (COUNTER) usage reports. In this column we quickly review the history of KBART from its inception in 2007 through the creation of the Phase 1 and Phase 2 best practices. Then we investigate how KBART can positively influence the quality of discovery services, ERM systems, and COUNTER. Finally, we put forward some suggestions on how KBART can become the key to standardizing the exchange of holdings data within the information supply chain to address needs beyond link resolvers and eliminate some redundant holdings maintenance tasks in libraries.     

Land of Linking

ABSTRACT

The electronic linking of scholarly materials has increasingly become both confusing and exciting. An understanding of the basics of linking technologies such as the OpenURL and the Digital Object Identifier (DOI) allows librarians to better serve their constituencies. Vendors also continue to offer linking alternatives to those libraries unable to purchase link resolvers, and a knowledge of these alternatives permits librarians to make informed decisions about a comprehensive and integrated linking plan for their libraries' electronic resources.     

ITEMS RECEIVED

No abstract available for this article.     

Two-stage NER for tweets with clustering

One main challenge of Named Entities Recognition (NER) for tweets is the insufficient information in a single tweet, owing to the noisy and short nature of tweets. We propose a novel system to tackle this challenge, which leverages redundancy in tweets by conducting two-stage NER for multiple similar tweets. Particularly, it first pre-labels each tweet using a sequential labeler based on the linear Conditional Random Fields (CRFs) model. Then it clusters tweets to put tweets with similar content into the same group. Finally, for each cluster it refines the labels of each tweet using an enhanced CRF model that incorporates the cluster level information, i.e., the labels of the current word and its neighboring words across all tweets in the cluster. We evaluate our method on a manually annotated dataset, and show that our method boosts the F1 of the baseline without collectively labeling from 75.4% to 82.5%.     

论加强学生主体性德育及其实施对策

学生德育是学校教育中一个非常重要的方面。本文针时传统学生德育模式中存在的非主体枉问题。提出了学生主体性德育模式，并从德育的主体、德育的理念、德育的目标及德育的方法等四个方面比较了学生主体性德育与传统德育的主要区别，提出了通过建立新型的师生关系、调控德育目标、拓展德育内容、更新德育方法等实施学生主体性德育的时策。