在实体与虚拟馆藏资源中多层面揭示文本

本文探索如何将实体和虚拟馆藏中现代文学资源进行归纳和综合,提取词的多种意义,分析意象、引申和感情色彩,揭示文本的多层面,从而加工成数字资源和文本检索工具书,以便提供给读者作选用的参考依据。     

Multilingual modeling of cross-lingual spelling variants

Technical term translations are important for cross-lingual information retrieval. In many languages, new technical terms have a common origin rendered with different spelling of the underlying sounds, also known as cross-lingual spelling variants (CLSV). To find the best CLSV in a text database index, we contribute a formulation of the problem in a probabilistic framework, and implement this with an instance of the general edit distance using weighted finite-state transducers. Some training data is required when estimating the costs for the general edit distance. We demonstrate that after some basic training our new multilingual model is robust and requires little or no adaptation for covering additional languages, as the model takes advantage of language independent transliteration patterns. We train the model with medical terms in seven languages and test it with terms from varied domains in six languages. Two test languages are not in the training data. Against a large text database index, we achieve 64–78 % precision at the point of 100% recall. This is a relative improvement of 22% on the simple edit distance.     

Optimization of some factors affecting the performance of query expansion

This paper examines the factors affecting the performance of global query expansion based on term co-occurrence data and suggests a way to maximize the retrieval effectiveness. Major parameters to be optimized through experiments are term similarity measure and the weighting scheme of additional terms. The evaluation of four similarity measures tested in query expansion reveal that mutual information and Yule's Y, which emphasize low frequency terms, achieve better performance than cosine and Jaccard coefficients that have the reverse tendency. In the evaluation of three weighting schemes, similarity weight performs well only with short queries, whereas fixed weights of approximately 0.5 and similarity rank weights were effective with queries of any length. Furthermore, the optimal similarity rank weight achieving the best overall performance seems to be the least affected by test collections and the number of additional terms. For the efficiency of retrieval, the number of additional terms needs not exceed 70 in our test collections, but the optimal number may vary according to the characteristics of the similarity measure employed.     

试论替换中项法——对三段论方法的反思与重建

词项逻辑特别是其中的核心部分直言三段论教学内容和方法的革新势在必行。现有的三段论方法本身都存在问题,应该重新探索能克服现有三段论方法的缺陷、能够解决三段论所有问题的简易方法,以利于逻辑学通识教育。研究表明,替换中项法正是我们发现的这种方法。     

University–industry interactions in applied research: The case of microelectronics

In the first part of the paper we discuss the pattern of research in microelectronics, illustrating the reasons why, in order to achieve distinguished scientific performance, universities need to collaborate with industry, and in particular academic researchers need to interact with industrial ones by face-to-face knowledge exchanges. In the second part, using patent data integrated with information collected through interviews, we measure the extent and intensity of the ties of academic with industrial researchers, and apply social network analysis to reconstruct the network of collaborations. The picture that emerges (from this Italian case) is fully consistent with the specific research pattern. Collaboration is based on teams of researchers from the two spheres, and strong connections are associated with high scientific performance. Moreover, border-crossing collaborations tend to be driven by cognitive proximity and personal relationships.     

Dependency structure language model for topic detection and tracking

In this paper, we propose a new language model, namely, a dependency structure language model, for topic detection and tracking (TDT) to compensate for weakness of unigram and bigram language models. The dependency structure language model is based on the Chow expansion theory and the dependency parse tree generated by a linguistic parser. So, long-distance dependencies can be naturally captured by the dependency structure language model. We carried out extensive experiments to verify the proposed model on topic tracking and link detection in TDT. In both cases, the dependency structure language models perform better than strong baseline approaches.     

基于句法依赖关系模板的术语相似度计算方法

针对现有基于语境特征的术语相似度算法在语境模板生成和匹配过程中存在的不足,提出基于术语的句法依赖关系自动构造术语语境模板,进而通过语境模板匹配计算术语相似度的方法。该方法既能减少语境模板的生成和匹配困难,又将术语语境特征较好地保留在模板中。针对新方法提出具体的实现步骤,并选取基因工程领域实验数据对新方法和现有典型方法进行对比评测。实验证明,新方法在计算效果方面具有明显提升。     

基于关键词分析的叙词表词间等同关系探讨

本文以"非典型肺炎"和"甲型H1N1流感"两个传染病作为研究样本,以中国知网(CNKI)数据库中的文献关键词作为语料库来源,基于用户关键词词频统计,并结合人工核查,分析同一概念的同义词和近义词以确立关键词词间的等同关系。目的是了解用户关键词和MeSH主题词之间的差异,分析用户表达偏好和习惯,进而为叙词表维护中的候选词汇选择提供一定的参考。     

汉语科技词系统研究进展

汉语科技词系统是科技词汇知识深层次组织与应用服务工具的集成体系。文章首先概述了词系统的内涵,然后总结了三年来在词汇自动获取、词空间构建和词聚类以及词系统的复杂网络分析评价上所取得的技术突破,并展望了科技词系统下一步的重点研发方向。     

共词分析过程中的若干问题研究

为完善和优化共词分析方法,本文从共词分析过程中概念术语的词源选择、高频词的选定、术语相关性计算以及多元统计分析四个方面系统地总结共词分析存在的局限性。在词源选择方面,论述不同类型的文献分析单元、术语的规范化以及术语表征差异性对共词分析的影响;在高频词选定方面,分析国内外相关研究在设定高频词阈值、考虑术语语义类型特征以及低频