展示中华民族博大精深的文化精髓——序刘业超《文心雕龙通论》

刘业超先生的《文心雕龙通论》,通过对龙著的历史文化解读,现代文化解读和世界文化解读,对百年来龙学研究的理论成果进行系统性总结,全面展示了中华民族博大精深的文化精髓,并将其融入现代文化视野之中,成为当代人一项宝贵的理论资源,为中国特色的文论体系与美学理论体系的建设提供了知识论、认识论与方法论的依据。《文心雕龙通论》的出版,对于以理论阐述的科学形态加速龙学走向世界的进程,推动中外文化的广泛交流,帮助更多海外学人了解龙学,了解中华文化的博大精深的思想与智慧,大有裨益。     

Using semantic similarity to reduce wrong labels in distant supervision for relation extraction

Distant supervision (DS) has the advantage of automatically generating large amounts of labelled training data and has been widely used for relation extraction. However, there are usually many wrong labels in the automatically labelled data in distant supervision (Riedel, Yao, & McCallum, 2010). This paper presents a novel method to reduce the wrong labels. The proposed method uses the semantic Jaccard with word embedding to measure the semantic similarity between the relation phrase in the knowledge base and the dependency phrases between two entities in a sentence to filter the wrong labels. In the process of reducing wrong labels, the semantic Jaccard algorithm selects a core dependency phrase to represent the candidate relation in a sentence, which can capture features for relation classification and avoid the negative impact from irrelevant term sequences that previous neural network models of relation extraction often suffer. In the process of relation classification, the core dependency phrases are also used as the input of a convolutional neural network (CNN) for relation classification. The experimental results show that compared with the methods using original DS data, the methods using filtered DS data performed much better in relation extraction. It indicates that the semantic similarity based method is effective in reducing wrong labels. The relation extraction performance of the CNN model using the core dependency phrases as input is the best of all, which indicates that using the core dependency phrases as input of CNN is enough to capture the features for relation classification and could avoid negative impact from irrelevant terms.     

Neural embedding-based indices for semantic search

Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus.     

A knowledge-based semantic framework for query expansion

Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets.     

Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.     

基于主题词表的数字档案馆概念搜索引擎的设计与实现

本文针对数字档案馆的现实状况,从数字档案馆馆内检索服务入手,提出了基于主题词表的概念检索方式,可以对用户输入的关键词进行规范化处理并进行概念上的扩检,提高了系统的检准率和检全率,并在此基础上设计了用户反馈机制,进一步完善了系统。     

用VB实现文件查找的几种方法

现今电脑硬盘的容量越来越大,其中储存的文件也越来越多.开发能够实现文件快速查找功能的应用程序,必将具有广泛的应用前景.利用VB6.0提供的强大文件操作对象(File System Object),编制了几种切实可行的文件快速查找程序.     

Web Design Trends in Academic Libraries—A Longitudinal Study

This study examines several key design elements of 37 academic library Web sites (members of the Association of Southeastern Research Libraries) and how they have changed between 2012 and 2015. While several studies have reviewed library Web sites looking for common design elements and content, the present work may be the first to look at the design elements of a large group of library Web sites over time. This study has two main goals: to present an objective analysis of the navigation and search interface designs at research university library Web sites, and to assess the usage of Web-scale discovery systems and content management systems in libraries. Other areas explored include when sites were most recently redesigned and whether sites have adopted a mobile-friendly, responsive design. Notable findings include a very high usage of Web-scale discovery systems, an increasing adoption of open source content management systems, and increasing implementation of responsive design. Also noted was a strong and growing standardization in navigation design. This study concludes with a review of the trends and discussion of current design patterns in academic library Web sites.     

高校图书馆读者流失问题分析

随着网络的发展,越来越多的读者使用网络搜索引擎作为获取信息服务的入口,许多高校图书馆都面临不同程度的到馆读者减少的问题。通过对高校读者使用信息资源情况的分析,笔者认为目前使用高校图书馆的读者人数并没有明显的减少,图书馆应该更多关注改进信息服务的方式,以适应新环境下读者对信息服务方式的要求。     

Affective Experiences of International and Home Students During the Information Search Process

An increasing number of students are studying abroad requiring that they interact with information in languages other than their mother tongue. The UK in particular has seen a large growth in international students within Higher Education. These nonnative English speaking students present a distinct user group for university information services, such as university libraries. This article presents the findings from an in-depth study to understand differences between the search processes of home and international students. Data were collected using an online survey and diary-interview to capture thoughts and feelings in a more naturalistic way. International students are found to have similar information search processes to those of home students, but sometimes face additional difficulties in assessing search results such as confusion when dealing with differing cultural perspectives. The potential implications for information service providers, particularly university libraries, are discussed, such as providing assistance to students for identifying appropriate English sources.