社会化标注系统中个性化信息推荐多维度融合与优化模型研究

[目的/意义]在社会化标注系统自组织运行的基础上，构建个性化信息推荐的多维度融合与优化模型，进而在大数据环境下，为用户提供精准的个性化信息推荐服务，从而进一步丰富个性化信息推荐的理论体系以及拓展个性化信息推荐的研究方法。[方法/过程]首先，对每一种个性化信息推荐方法的优点和不足进行深入分析；然后，将基于图论（社会网络关系）、基于协同过滤以及基于内容（主题）3种个性化信息推荐方法进行多维度深度融合，构建个性化信息推荐多维度融合模型；最后，对社会化标注系统中个性化信息推荐多维度融合模型进行优化，从而解决个性化推荐过程中用户"冷启动"、数据稀疏性和用户偏好漂移等问题。[结果/结论]通过综合考虑现有的基于图论（社会网络关系）、基于协同过滤以及基于内容（主题）的个性化信息推荐方法各自的贡献和不足，实现3种方法之间的多维度深度融合，并结合心理认知、用户情境以及时间、空间等优化因素，最终构建出社会化标注系统中个性化信息推荐多维度融合与优化模型。     

AdaSL: An Unsupervised Domain Adaptation framework for Arabic multi-dialectal Sequence Labeling

Dialectal Arabic (DA) refers to varieties of everyday spoken languages in the Arab world. These dialects differ according to the country and region of the speaker, and their textual content is constantly growing with the rise of social media networks and web blogs. Although research on Natural Language Processing (NLP) on standard Arabic, namely Modern Standard Arabic (MSA), has witnessed remarkable progress, research efforts on DA are rather limited. This is due to numerous challenges, such as the scarcity of labeled data as well as the nature and structure of DA. While some recent works have reached decent results on several DA sentence classification tasks, other complex tasks, such as sequence labeling, still suffer from weak performances when it comes to DA varieties with either a limited amount of labeled data or unlabeled data only. Besides, it has been shown that zero-shot transfer learning from models trained on MSA does not perform well on DA. In this paper, we introduce AdaSL, a new unsupervised domain adaptation framework for Arabic multi-dialectal sequence labeling, leveraging unlabeled DA data, labeled MSA data, and existing multilingual and Arabic Pre-trained Language Models (PLMs). The proposed framework relies on four key components: (1) domain adaptive fine-tuning of multilingual/MSA language models on unlabeled DA data, (2) sub-word embedding pooling, (3) iterative self-training on unlabeled DA data, and (4) iterative DA and MSA distribution alignment. We evaluate our framework on multi-dialectal Named Entity Recognition (NER) and Part-of-Speech (POS) tagging tasks.The overall results show that the zero-shot transfer learning, using our proposed framework, boosts the performance of the multilingual PLMs by 40.87% in macro-F1 score for the NER task, while it boosts the accuracy by 6.95% for the POS tagging task. For the Arabic PLMs, our proposed framework increases performance by 16.18% macro-F1 for the NER task and 2.22% accuracy for the POS tagging task, and thus, achieving new state-of-the-art zero-shot transfer learning performance for Arabic multi-dialectal sequence labeling.     

Web2.0环境下的个人知识管理初探——以folksonomy为例

文章在分析几种现有的个人知识管理工具的基础上,以folksonomy为例探索适应Web2.0环境的个人知识管理方法与途径。指出folksonomy适应Web2.0环境下个人知识管理的可行性及现实意义,并提出基于folksonomy的个人知识管理的实现途径。     

社会化标注系统中用户标注动机差异分析

随着社会化标注系统应用的普及,社会化标签为用户的信息检索和信息组织提供了便利的平台,而现有的对社会化标签的研究关注应用层面的较多,较少探讨用户标注内在机制问题,而通过对用户标注动机的探究,可以指导社会化标注系统更好的满足用户的需要.因此本文采用自行研制的用户标注动机量表,通过调查社会化标注系统中有过标注行为用户的标注动机,从不同性别、不同年龄、不同学历、不同职业、不同社会化标注系统使用时间以及使用次数、不同标注资料类型7个方面分析比较不同背景用户标注动机的差异,最后为增强用户的标注动机提出相应的建议.     

Investigating effectiveness and user acceptance of semantic social tagging for knowledge sharing

Social tagging systems enable users to assign arbitrary tags to various digital resources. However, they face vague-meaning problems when users retrieve or present resources with the keyword-based tags. In order to solve these problems, this study takes advantage of Semantic Web technology and the topological characteristics of knowledge maps to develop a system that comprises a semantic tagging mechanism and triple-pattern and visual searching mechanisms. A field experiment was conducted to evaluate the effectiveness and user acceptance of these mechanisms in a knowledge sharing context. The results show that the semantic social tagging system is more effective than a keyword-based system. The visualized knowledge map helps users capture an overview of the knowledge domain, reduce cognitive effort for the search, and obtain more enjoyment. Traditional keyword tagging with a keyword search still has the advantage of ease of use and the users had higher intention to use it. This study also proposes directions for future development of semantic social tagging systems.     

基于社会标签的推荐系统研究

社会标签是一种新颖的大众索引方法,在Web 2.0时代各种收藏、检索、共享网站中得到广泛的应用,个性化推荐系统是基于用户的偏好为用户提供个性化信息服务的重要技术。本文针对推荐技术如何与社会标签结合的问题,分析了最新的研究现状和应用。详细阐述了3个方面的研究进展:标签推荐系统的研究、基于社会标签的个性化推荐系统的研究、社会标签的推荐应用系统。     

基于社会化标注的网络舆情参与者分析

在Web2.0环境下,社会化标注的出现给网络舆情分析提供了新的视角。首先对当前的网络舆论环境进行了分析,在此基础上,着重对舆情参与者个人、关键舆情参与者识别以及舆情参与者的社区划分三个方面进行了重点剖析,并给出了相应的思路与实施方法,最后给出了社会化标注环境下舆情控制的相关建议。     

基于条件随机场的汉语词性标注方法研究

本文提出了一种基于条件随机场模型的汉语词性标注方法。该方法能够充分利用词的上下文信息作为特征。实验表明,该方法的标注效果明显优于基于隐马尔可夫模型和最大熵马尔科夫模型的方法。     

词位标注汉语分词技术详解

近年来基于字的词位标注的方法极大地提高了汉语分词的性能,该方法将汉语分词转化为字的词位标注问题,借助于优秀的序列标注模型,基于字的词位标注汉语分词方法逐渐成为分词的主要技术路线。本文简要介绍了词位标注汉语分词的基本思想,探析了基于条件随机场实现词位标注汉语分词的机理,并对采用四词位标注集,使用CRF＋＋0.53工具包实现字串序列词位标注进行了详解。最后在Bakeoff2006的评测语料上进行了封闭测试。     

社会标签在图书馆的应用

社会标签在商业网站的成功应用及其特有的性质和功能,使它在图书馆的应用成为可能。图书馆领域对社会标签的应用大体可分为内嵌式服务模式和延伸式服务模式两种。内嵌式服务模式是指将社会标签直接引入和添加到图书馆的服务中;延伸式服务模式是指借助社会标签将图书馆的服务辐射出去。社会标签在图书馆的应用为用户提供了参与图书馆信息资源组织和建设的机会,并为知识共享做出积极的贡献。