Interest in an expository text: How does it flow from reading to writing?

This study investigated the flow of interest in a reading-to-write activity. It was hypothesized that (a) different contents in a text would stimulate different types of interest and (b) different writing tasks would impact differently students’ use of interesting text segments. Participants were 247 11th- and 12th-grade students, who were divided into 6 groups according to a 2 (interesting vs. uninteresting topic) × 3 (type of writing task) design. While reading, students rated novelty, curiosity, impressiveness, importance, and willingness to reflect on each text segment. Participants were then assigned to one of three writing tasks, namely argumentation, text designing, and composition. Results showed that concepts were regarded as important, events/activities as impressive, and evaluations as most worthy of reflection. Topic interest was explicitly expressed mainly in the argumentation condition, where increased topic interest scores after reading and writing were also found.     

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.     

一个基础教育网站搜索引擎的设计与实现

在研究网站元数据的基础上，介绍一个以基础教育网站为检索对象的搜索引擎系统。结合基础教育网站的特点，分析该系统的关键技术，如主题蜘蛛搜索、网站分类、网站信息提取等，并对系统的整体架构、功能模块进行详细描述。     

Identifying and removing duplicate records from systematic review searches

Objective

The purpose of this study was to compare effectiveness of different options for de-duplicating records retrieved from systematic review searches.

Methods

Using the records from a published systematic review, five de-duplication options were compared. The time taken to de-duplicate in each option and the number of false positives (were deleted but should not have been) and false negatives (should have been deleted but were not) were recorded.

Results

The time for each option varied. The number of positive and false duplicates returned from each option also varied greatly.

Conclusion

The authors recommend different de-duplication options based on the skill level of the searcher and the purpose of de-duplication efforts.     

Expert group formation using facility location analysis

In this paper, we propose an optimization framework to retrieve an optimal group of experts to perform a multi-aspect task. While a diverse set of skills are needed to perform a multi-aspect task, the group of assigned experts should be able to collectively cover all these required skills. We consider three types of multi-aspect expert group formation problems and propose a unified framework to solve these problems accurately and efficiently. The first problem is concerned with finding the top k experts for a given task, while the required skills of the task are implicitly described. In the second problem, the required skills of the tasks are explicitly described using some keywords but each expert has a limited capacity to perform these tasks and therefore should be assigned to a limited number of them. Finally, the third problem is the combination of the first and the second problems. Our proposed optimization framework is based on the Facility Location Analysis which is a well known branch of the Operation Research. In our experiments, we compare the accuracy and efficiency of the proposed framework with the state-of-the-art approaches for the group formation problems. The experiment results show the effectiveness of our proposed methods in comparison with state-of-the-art approaches.     

基于Single-Pass的网络话题在线聚类方法研究

基于Single-Pass算法思想,研究网络话题的在线聚类方法,以期及时捕捉网络信息的动态变化在分析该方法聚类流程的基础上,重点研究网络动态信息流的文本特征抽取和权重计算方法,以及话题类表示和更新等关键问题,设计实验对比分析不同的标题中特征加权系数、特征权重计算和标准化方法以及话题类向量维度对话题聚类质量和时间效率的影响。     

网络舆情监控系统中的主题帖自动标引及情感倾向分析研究

本研究以逸仙时空BBS为舆情信息源,对需要关注的帖子进行情感倾向性分析的探索性研究,设计了主题帖自动标引和情感倾向性分析策略,并对主题帖自动标引结果、倾向性人工判断与自动分析的结果进行对比。     

Topic discovery based on text mining techniques

In this paper, we present a topic discovery system aimed to reveal the implicit knowledge present in news streams. This knowledge is expressed as a hierarchy of topic/subtopics, where each topic contains the set of documents that are related to it and a summary extracted from these documents. Summaries so built are useful to browse and select topics of interest from the generated hierarchies. Our proposal consists of a new incremental hierarchical clustering algorithm, which combines both partitional and agglomerative approaches, taking the main benefits from them. Finally, a new summarization method based on Testor Theory has been proposed to build the topic summaries. Experimental results in the TDT2 collection demonstrate its usefulness and effectiveness not only as a topic detection system, but also as a classification and summarization tool.     

基于主题句相似度的标题党新闻鉴别技术研究

针对目前网络上的标题党新闻,提出一种标题党新闻自动识别的算法。通过分析新闻网页构成的特点,抽取出新闻标题和新闻正文;以句子关系矩阵为基础,提出一种以语句为单位的主题句抽取算法;根据句子相似度计算结果来进行判断。实验表明,本方法的识别精度可达到80%,是一种有效的方法。     

基于种子文档LDA话题的演化研究

提出一种基于种子文档的LDA话题演化方法。首先选取种子文档,利用种子文档指导后一时间段文档的建模,然后根据种子文档的语义分布信息对连续时间上的LDA话题进行关联,保证话题的同一性。实验结果证明,在NIPS论文语料集和全国两会新闻报道集中,该方法可以推导特定话题的演化结果,避免关联话题之间存在的演化结果。