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201.
Jamal Abdul Nasir Iraklis Varlamis Samreen Ishfaq 《Information processing & management》2019,56(5):1605-1617
Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets. 相似文献
202.
Minh-Tien Nguyen Viet Cuong Tran Xuan Hoai Nguyen Le-Minh Nguyen 《Information processing & management》2019,56(3):495-515
In the context of social media, users usually post relevant information corresponding to the contents of events mentioned in a Web document. This information posses two important values in that (i) it reflects the content of an event and (ii) it shares hidden topics with sentences in the main document. In this paper, we present a novel model to capture the nature of relationships between document sentences and post information (comments or tweets) in sharing hidden topics for summarization of Web documents by utilizing relevant post information. Unlike previous methods which are usually based on hand-crafted features, our approach ranks document sentences and user posts based on their importance to the topics. The sentence-user-post relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our proposed matrix co-factorization algorithm computes the score of each document sentence and user post and extracts the top ranked document sentences and comments (or tweets) as a summary. We apply the model to the task of summarization on three datasets in two languages, English and Vietnamese, of social context summarization and also on DUC 2004 (a standard corpus of the traditional summarization task). According to the experimental results, our model significantly outperforms the basic matrix factorization and achieves competitive ROUGE-scores with state-of-the-art methods. 相似文献
203.
Frank Z. Xing Filippo Pallucchini Erik Cambria 《Information processing & management》2019,56(3):554-564
Sentiment lexicons are essential tools for polarity classification and opinion mining. In contrast to machine learning methods that only leverage text features or raw text for sentiment analysis, methods that use sentiment lexicons embrace higher interpretability. Although a number of domain-specific sentiment lexicons are made available, it is impractical to build an ex ante lexicon that fully reflects the characteristics of the language usage in endless domains. In this article, we propose a novel approach to simultaneously train a vanilla sentiment classifier and adapt word polarities to the target domain. Specifically, we sequentially track the wrongly predicted sentences and use them as the supervision instead of addressing the gold standard as a whole to emulate the life-long cognitive process of lexicon learning. An exploration-exploitation mechanism is designed to trade off between searching for new sentiment words and updating the polarity score of one word. Experimental results on several popular datasets show that our approach significantly improves the sentiment classification performance for a variety of domains by means of improving the quality of sentiment lexicons. Case-studies also illustrate how polarity scores of the same words are discovered for different domains. 相似文献
204.
Human collaborative relationship inference is a meaningful task for online social networks and is called link prediction in network science. Real-world networks contain multiple types of interacting components and can be modeled naturally as heterogeneous information networks (HINs). The current link prediction algorithms in HINs fail to effectively extract training samples from snapshots of HINs; moreover, they underutilise the differences between nodes and between meta-paths. Therefore, we propose a meta-circuit machine (MCM) that can learn and fuse node and meta-path features efficiently, and we use these features to inference the collaborative relationships in question-and-answer and bibliographic networks. We first utilise meta-circuit random walks to obtain training samples in which the basic idea is to perform biased meta-path random walks on the input and target network successively and then connect them. Then, a meta-circuit recurrent neural network (mcRNN) is designed for link prediction, which represents each node and meta-path by a dense vector and leverages an RNN to fuse the features of node sequences. Experiments on two real-world networks demonstrate the effectiveness of our framework. This study promotes the investigation of potential evolutionary mechanisms for collaborative relationships and offers practical guidance for designing more effective recommendation systems for online social networks. 相似文献
205.
206.
Kai Hu Qing Luo Kunlun Qi Siluo Yang Jin Mao Xiaokang Fu Jie Zheng Huayi Wu Ya Guo Qibing Zhu 《Information processing & management》2019,56(4):1185-1203
Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations. 相似文献
207.
任辉 《荆门职业技术学院学报》2004,19(2):87-90
稿约规则中关于回复时限的条款在某些方面存在着不足,在实际操作中易造成作者合法权利的损害。因此,修改回复时限、理顺出版者与作者间的权利和义务关系十分必要。解决这个问题,可从导致审稿时间过长的“瓶颈”——审稿过程入手,做到工作规范,审稿高效,退稿及时。 相似文献
208.
This paper analyzes multiple factors from current university students' high school experiences, including demographic, educational, and economic factors, and current standing and grade point average (GPA), to evaluate the students' information literacy skills associated with a 1000 level course on information literacy which is part of the university's general education requirement. The pre-test indicates that students lack sufficient skills needed to do college-level research. Results of regression analyses demonstrate that only current university GPA and standardized test scores have any influence on information literacy test scores. 相似文献
209.
210.
基于普赖斯定律和二八定律及在线投稿系统构建某科技期刊核心作者用户库 总被引:1,自引:0,他引:1
以某刊为例,根据普莱斯定律及2013-2015年在某刊的投稿情况设定满足条件:1)2013-2015年发文量≥2篇;2)A:2013-2015年连续3年均在该刊投稿;B:2014年或2015在该刊投稿量≥2篇的作者用户(满足条件2中的A或B均可)为高发文量且对投稿某刊有一定青睐程度的核心作者候选用户.根据二八定律确定文章被引频次或下载频次在各年排名前20%左右的文章作者为具有高影响力的核心作者候选人.将既满足高发文量、高影响力且对投稿某刊有一定青睐程度的作者用户作为该刊的核心作者用户群,构建核心作者用户库,通过为该批用户提供更加优质服务激发他们的写作热情和投稿热情,以吸引优质稿件.最终纳入74名作者用户为该刊的核心作者用户群,共投稿306篇,录用200篇,最高录用比100%,最低28.6%,平均65.4%,其中下载频次或被引频次在各年进入前20%的文章总数为99篇,占比49.5%,最高被引频次14次,最高下载频次314次. 相似文献