基于多元关系融合的科技文本主题识别方法研究

当前文本主题获取方法大多依靠单一关联分析,不能全面分析可获取信息,难以准确获取科技发展主题。科技文献的主题词、作者和引文之间蕴含了以研究主题内容为纽带的语义关联关系,主题词共现关系、引文关系和合著关系分别从不同的角度展现了主题关联关系。因此,本文根据主题词之间语义关系距离的远近,将主题识别中主题词关联分为基础关系、强化关系和新增关系,在此基础上提出面向主题识别的多元关系抽取及关系融合方法;并以基因工程疫苗的研发与制备领域为例进行领域实证分析,利用PathSelClus算法实现基于多元关系融合的主题聚类,通过对比实验证明多元关系融合可以有效提高实证领域的文本主题聚类效果,而未来多关系融合主题识别则是需要重点关注的问题。图4。表6。参考文献19。     

The Role of Family Verbal Aggression and Taking Conflict Personally in Romantic Relationship Complaint Avoidance

This study examined complaint avoidance in adult romantic relationships as a function of both exposure to family verbal aggression in childhood and taking conflict personally. Four hundred thirty-seven college students completed measures assessing their histories of family verbal aggression, complaint avoidance behaviors, and tendencies to take conflict personally. Results indicated that a history of family verbal aggression and three components of taking conflict personally, namely positive relational effects, negative relational effects, and like/dislike valence, were negatively associated with complaint avoidance. In addition, a history of family verbal aggression was positively associated with positive relational effects, negative relational effects, and like/dislike valence. The relationship between a history of family verbal aggression and complaint avoidance, however, was not mediated by taking conflict personally.     

主题模型可视化研究综述

在大数据背景下,以LDA为代表的主体模型数据挖掘技术得到了飞速的发展。随着研究的深入和科技工作者对结果可理解要求的提高,主题模型的可视化也成为了研究的热点。本文将主题可视化分为两类：结语文档集内容的主题模型可视化和融合外部特征的主体模型可视化,本文在前人的基础上,总结了上述两类的主体模型可视化,分析了各个可视化工具的缺点,并对其进行了客观的评价。也以后的主题模型可视化提出了一些建议并进行了展望。     

主题型酒店：发展、问题与策略

本文论述了主题酒店会在我国发展的成因,指出存在的主题酒店与特色酒店被视为一物;酒店在进行主题选择时,往往忽视与其所在她域的形象相协调等问题。最后从“蓝海战略”反思本土主题酒店的经营,阐述了对策与措施;按“剔除-减少-增加-创造”模式创造价值,并不断创新发展,打造出主题型酒店牢固的合力与强大的实力;开拓顾客群;打造品牌,长期留住顾客;建立电子信息系统,加强与利益相关者的沟通。     

庄子寓言故事话题引入研究—与现代汉语叙述故事话题引入比较

通过对庄子寓言故事的研究,考察其话题引入的句法和形态特点,发现:庄子寓言故事中话题引入的句法形式有存现结构和非存现结构两种,存现结构作为话题标示的句法手段古今一致;庄子寓言故事中话题标示的句法手段和形态手段独立使用;指示词"夫"用来标示话题,是庄子寓言故事话题引入的形态手段之一,光杆名词短语或光杆名词也可以直接充当话题;古汉语中特有的短语形式充当话题,比如"者"字短语、"之"字短语等;庄子寓言故事中多采用零形回指和代词回指的形式回指引入话题,未见名词短语的回指方式.     

基于Lucene的互联网舆情监测系统设计与实现

互联网舆情监测系统采用搜索引擎“Lucene”实现互联网数据的爬取与检索,通过舆情分析引擎实现舆情的话题检测和热点发现,最后用户通过客户端进行舆情监测。文章详细介绍了该系统的体系结构、功能模块及其关键技术。     

Weighted Word Pairs for query expansion

This paper proposes a novel query expansion method to improve accuracy of text retrieval systems. Our method makes use of a minimal relevance feedback to expand the initial query with a structured representation composed of weighted pairs of words. Such a structure is obtained from the relevance feedback through a method for pairs of words selection based on the Probabilistic Topic Model. We compared our method with other baseline query expansion schemes and methods. Evaluations performed on TREC-8 demonstrated the effectiveness of the proposed method with respect to the baseline.     

师范生教学能力培养之“同课异构”模式

在培养高师师范生教学实践能力的过程中存在理论与实践脱节,缺乏有价值的引领和同伴的有效互助等诸多问题。"同课异构"训练模式能有效针对传统培养中的弊端,培养新课程改革需要的研究型、反思型教师,有效培养师范生的团队、协作能力。"同课异构"模式实施时应选择具有教学研究价值和适合师范生试讲的课题,训练时和微格教学、说课训练有机结合。     

Graph neural topic model with commonsense knowledge

Traditional topic models are based on the bag-of-words assumption, which states that the topic assignment of each word is independent of the others. However, this assumption ignores the relationship between words, which may hinder the quality of extracted topics. To address this issue, some recent works formulate documents as graphs based on word co-occurrence patterns. It assumes that if two words co-occur frequently, they should have the same topic. Nevertheless, it introduces noise edges into the model and thus hinders topic quality since two words co-occur frequently do not mean that they are on the same topic. In this paper, we use the commonsense relationship between words as a bridge to connect the words in each document. Compared to word co-occurrence, the commonsense relationship can explicitly imply the semantic relevance between words, which can be utilized to filter out noise edges. We use a relational graph neural network to capture the relation information in the graph. Moreover, manifold regularization is utilized to constrain the documents’ topic distributions. Experimental results on a public dataset show that our method is effective at extracting topics compared to baseline methods.     

ATM: Adversarial-neural Topic Model

Topic models are widely used for thematic structure discovery in text. But traditional topic models often require dedicated inference procedures for specific tasks at hand. Also, they are not designed to generate word-level semantic representations. To address the limitations, we propose a neural topic modeling approach based on the Generative Adversarial Nets (GANs), called Adversarial-neural Topic Model (ATM) in this paper. To our best knowledge, this work is the first attempt to use adversarial training for topic modeling. The proposed ATM models topics with dirichlet prior and employs a generator network to capture the semantic patterns among latent topics. Meanwhile, the generator could also produce word-level semantic representations. Besides, to illustrate the feasibility of porting ATM to tasks other than topic modeling, we apply ATM for open domain event extraction. To validate the effectiveness of the proposed ATM, two topic modeling benchmark corpora and an event dataset are employed in the experiments. Our experimental results on benchmark corpora show that ATM generates more coherence topics (considering five topic coherence measures), outperforming a number of competitive baselines. Moreover, the experiments on event dataset also validate that the proposed approach is able to extract meaningful events from news articles.