Google应用技巧

文章详细分析了网络搜索引擎Google 的功能特征, 讨论了在使用中存在的一些现象和问题。通过具体例证说明: 在进行组合查询时, 要对所有的查询词分别加引号; Google 在对中文繁简字体语义转换时, 有可能出现明显的错误; 搜索到的数据在同一范围内、在以小时为单位内也会出现巨大的变化; 以及在不同范围内, 搜索同一信息时, 随时间的不同, 搜索到的结果会有不同的排序。     

语义网研究的基本问题

概述目前我国语义网的研究现状，在简要介绍语义网概念和模型的基础上，着重分析当前我国语义网的四大研究热点，即语义网描述语言比较研究、元数据研究、本体层开发研究和智能信息检索研究，并阐述语义网的发展对数字图书馆建设带来的若干启示。     

Analysis of Statistical Question Classification for Fact-Based Questions

Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.     

基于词向量扩展的学术资源语义检索技术

[目的/意义] 尝试以统计的方法为指导思想,探究基于词向量扩展的语义检索技术来提升学术资源的语义检索能力。[方法/过程] 利用自然语言处理、文本挖掘技术,对采集来的学术资源（主要是学术论文）元数据进行预处理,结合word2vec词向量生成工具和elasticsearch全文检索引擎搭建语义检索系统,对学术资源进行语义检索的探索研究。[结果/结论] 本文提出的方法能够有效提升学术信息的检索效果,一定程度上实现学术资源的语义检索,并为后续语义检索的进一步研究提供借鉴。     

基于SKOS的学术期刊文本资源多粒度语义标注方法研究

[目的/意义] 针对学术期刊文本资源语义标注仍存在的通用本体难以构建、标注粒度单一两大问题,提出基于SKOS的学术期刊多粒度语义标注方法,从而进一步推进语义标注的应用发展,更好满足用户的多粒度学术信息需求。[方法/过程] 在对《中国汉语主题词表》进行SKOS描述的基础上,以学术期刊文本资源为对象,实现其多粒度语义标注,并通过实证研究验证该方法的可行性。[结果/结论] 利用SKOS实现对学术期刊文本资源进行多粒度语义标注,较之当前学术检索系统中的标注结果,在"查全"查准"内部特征检索入口"检索结果反馈形式"4个方面具有一定优势。     

Spatial Discovery of Linked Research Datasets and Documents at a Spatially Enabled Research Library

Current publishing practices in academia tend to result in datasets that are difficult to discover. This is because datasets are not well-integrated across academic domains and they are often not linked to the documents that reference them. For these reasons, discovering datasets across domains can be challenging; for example, discovering archeological observations and biological specimens using the same search is not widely supported, even if both datasets share a similar spatial extent, like Mesoamerica. It is also challenging to retrieve relevant documents that reference datasets; for example, retrieving a series of field reports that reference archeological observations is typically not supported. Our work develops an extensible method for: (1) geographically integrating collections across disciplinary repositories and (2) connecting datasets to related documents. We describe a collection of spatially-referenced researcher datasets, capturing their metadata elements and encoding them as linked open data. We then leverage existing library services to formalize links from datasets to documents. The system described in this work has been deployed, resulting in an experimental open data site for the UCSB campus. Results indicate that this system can be scaled-up with support from an institutional repository in the near future.     

重言管窥

重言源远流长,应用广泛普遍。它结构独特,灵活多样;它短小凝炼,鲜明生动,往往起到画龙点睛的作用。通过重言来构词或构形,是汉语悠久的历史传统,也是具有鲜明民族特色的语汇、语法现象。在文学创作中,重言运用一直被当作主要的语言表现手法之一。重言的意义不仅表现在语言、文学上,而且渗进更广泛、深刻而根本的中国“社会传统”和“心理习惯”之中。它成为一种文化现象,对中国语言及文学、文化的历史、现实与未来产生重要的影响。     

技术知识的跨领域应用及潜在技术方案的识别

[目的/意义]针对企业实施技术创新战略时所面临的技术发展趋势不明朗、技术瓶颈难以突破的问题,提出一种跨领域寻找潜在技术方案的研究方法,引导企业利用跨领域研究方法解决复杂技术问题,保证技术创新的顺利进行。[方法/过程]以知识在技术领域间的流动为切入点,引入语义TRIZ中的知识发现理论,将专利摘要中抽取的SAO结构分别与P&S主题、技术领域进行匹配,绘制技术地图,描述技术知识跨领域应用现状,识别潜在技术方案。[结果/结论]以3D生物打印技术为例进行的实证分析表明,随着技术的不断发展,技术问题和技术方案的种类不断增加,技术问题在追加技术领域中的分布较为分散,而技术方案在主要技术领域中的分布更为集中。识别出的潜在技术方案具有一定的合理性和可靠性,可为企业技术创新活动的顺利进行提供更为清晰的指导和决策支持。     

Using LSA and text segmentation to improve automatic Chinese dialogue text summarization

Automatic Chinese text summarization for dialogue style is a relatively new research area. In this paper, Latent Semantic Analysis （LSA） is first used to extract semantic knowledge from a given document, all question paragraphs are identified, an automatic text segmentation approach analogous to Text＇filing is exploited to improve the precision of correlating question paragraphs and answer paragraphs, and finally some ＂important＂ sentences are extracted from the generic content and the question-answer pairs to generate a complete summary. Experimental results showed that our approach is highly efficient and improves significantly the coherence of the summary while not compromising informativeness.     

语义分类的学科主题演化分析方法研究——以我国图书情报领域大数据研究为例

[目的/意义] 学科主题演化研究有助于掌握学科发展现状、研究热点、研究前沿和发展趋势等情况,是进行科技创新的基础,是面向科技创新的重要研究方向。[方法/过程] 提出一种语义分类的学科主题演化分析方法：将关键词分为研究问题、研究方法和研究技术3类,构建不同语义分类的共词网络;然后基于Fast Unfolding社区发现算法识别具有语义特征的社区（主题）;利用相似度算法计算相邻子时期主题间的相似度,构建学科主题演化图谱,以分析某学科领域研究问题、研究方法和研究技术的变化,实现深度、细致的学科主题演化分析。[结果/结论] 通过对2012-2015年CNKI数据库收录的我国大数据研究领域相关论文数据的处理分析,证明该方法的准确性和有效性。