基于社会化标签系统的个性化信息推荐探讨

针对用户个人特征并向其提供准确恰当信息的个性化信息推荐研究,一直是学术界和产业界所关注的热点。结合后控词表,对用户分散的、个性化的标注进行处理,并将用户兴趣用向量表示,然后借鉴协同过滤算法的思想,寻找出相似用户集及其内部的资源集。在此基础上,采用相对匹配策略,提出一种基于社会化标签系统的个性化推荐方法。     

社会标签进展研究概述

近年来,社会标签的发展非常迅速,在信息组织和信息构建领域有着广泛的应用。社会标签的进展主要归纳为两个方面：①在各个领域中的应用进展;②与传统知识组织体系的关系探讨和融合研究。社会标签与知识组织体系的融合模式主要分为三种：①两者之间直接进行映射;②从分类表中抽取上下级关系和相关关系;③将分面分类的理论与方法移植到社会标签上,达到对标签词的组织和呈现。     

Return to Babel: Emergent Diversity, Digital Resources, and Local Knowledge

Information system research that recognizes, supports, and enables access of diverse knowledge communities online has become a major concern. Of particular concern is how these digital resources can support and preserve local identities and promote grass-roots involvement in creating the infrastructure of devolved governance. The aim of this article is to expose some of the conceptual obstacles to programs of devolved and local knowledge resources, and to provide an account of agency that recognizes that online communities, and community identity, are essential for eliciting, managing, and sharing local knowledges. Further, this article demonstrates how the implications of these issues impact institutions for managing knowledge that may be extended to all sorts of knowledge communities.     

基于国外图书情报领域六种主流期刊的网络信息行为研究综述

本文选取国外图书情报领域六种主流期刊为研究对象.首先跟踪这些期刊2003～ 2012年每期发表的与网络信息行为有关的研究论文,经过仔细筛选后得到405篇文献,通过关键词词频统计展现词频为5及以上的所有关键词,再从行为类型和环境、信息行为主体、研究方法和理论基础、网络信息行为影响因素等方面对这些关键词进行分析,从而展现网络信息行为研究的重点和热点.然后,针对这六种期刊,结合词频统计,利用内容分析法,创新性地从信息源选择行为、信息搜寻行为、信息交流行为、信息使用行为、社会化标注行为、移动信息行为等方面,进一步揭示研究的热点和趋势.     

基于HMM的楚辞自动分词标注研究

研究古代和现代汉语的自动分词标注技术,用隐马尔科夫模型对《楚辞》进行自动分词标注实验,通过比较分词后的标注词性概率,取最大概率作为最后的分词和词性标注结果,并在其中使用全切分和加值平滑算法。经过实验调整分词标注程序模块和参数,最终得到一个分词标注辅助软件,其开放测试的分词F值为85%,标注F值为55%,高出基准F值14个百分点。     

Pronounce differently,mean differently: A multi-tagging-scheme learning method for Chinese NER integrated with lexicon and phonetic features

Named Entity Recognition (NER) aims to automatically extract specific entities from the unstructured text. Compared with performing NER in English, Chinese NER is more challenging in recognizing entity boundaries because there are no explicit delimiters between Chinese characters. However, most previous researches focused on the semantic information of the Chinese language on the character level but ignored the importance of the phonetic characteristics. To address these issues, we integrated phonetic features of Chinese characters with the lexicon information to help disambiguate the entity boundary recognition by fully exploring the potential of Chinese as a pictophonetic language. In addition, a novel multi-tagging-scheme learning method was proposed, based on the multi-task learning paradigm, to alleviate the data sparsity and error propagation problems that occurred in the previous tagging schemes, by separately annotating the segmentation information of entities and their corresponding entity types. Extensive experiments performed on four Chinese NER benchmark datasets: OntoNotes4.0, MSRA, Resume, and Weibo, show that our proposed method consistently outperforms the existing state-of-the-art baseline models. The ablation experiments further demonstrated that the introduction of the phonetic feature and the multi-tagging-scheme has a significant positive effect on the improvement of the Chinese NER task.     

Connotea中Social Tagging机制研究

选取Connotea为研究对象，统计分析标签、被标引资源、标引者两两关联关系，发现标签覆盖资源范围较广，标签共现现象突出，部分用户标引活跃，资源平均标签数较低，用户对内容关注度规律性转移，科研领域的用户比较多地使用规范词。提出增加标引词结构关联和细粒内容定位标引。     

最新标签本体：标签和民俗分类法语义模型

当前,人们越来越关注协作化标签系统中标签数据的表示和共享。传统的标签（指随意标注的、没有与结构化本体相关联的标签）由于语言和语法的差异以及录入错误等,不适于做协作化处理。而且标签反映的是用户个人的观点,也不对同义词、词形变化或者其他对照映射关系做规范处理。因此笔者认为,标签的常规处理方法对语义协作的贡献非常有限,即便是有,要让计算机实现自动共享也是极其困难的。在语义层面上讨论数种协作标签标注的方法,提出一个标签协作标注和设计民俗分类法的概念模型,同时比较已有的标签本体,提出一个评价标准,并参照该标准讨论这些标签本体的优缺点。