Large-scale News Research Articles

Stream-based live entity resolution approach with adaptive duplicate count strategy

Recently, researchers have been more concerned about large-scale news and tweet data generated by the social media. Some cloud service providers utilise the data to find public sentiments for the tenants. The challenge is how to clean the big data in the cloud before making further analysis. To address this issue, we propose a new live entity resolution approach at a time to find duplicates from the news and tweet data. We investigate possible solutions to address live entity resolution in the cloud, to make sliding window size adaptive using multistep distance and window size dependent duplicate count strategy with alterable window step, and find duplicates by overlapping boundary objects in adjacent blocks. Finally, our experimental evaluation based on the news data on large datasets shows the high effectiveness and efficiency of the proposed approaches.

Stream-based live entity resolution approach with adaptive duplicate count strategy

Recently, researchers have been more concerned about large-scale news and tweet data generated by the social media. Some cloud service providers utilise the data to find public sentiments for the tenants. The challenge is how to clean the big data in the cloud before making further analysis. To address this issue, we propose a new live entity resolution approach at a time to find duplicates from the news and tweet data. We investigate possible solutions to address live entity resolution in the cloud, to make sliding window size adaptive using multistep distance and window size dependent duplicate count strategy with alterable window step, and find duplicates by overlapping boundary objects in adjacent blocks. Finally, our experimental evaluation based on the news data on large datasets shows the high effectiveness and efficiency of the proposed approaches.

Content-Based Publish/Subscribe System for Web Syndication

Content syndication has become a popular way for timely delivery of frequently updated information on the Web. Today, web syndication technologies such as RSS or Atom are used in a wide variety of applications spreading from large-scale news broadcasting to medium-scale information sharing in scientific and professional communities. However, they exhibit serious limitations for dealing with information overload in Web 2.0. There is a vital need for efficient real-time filtering methods across feeds, to allow users to effectively follow personally interesting information. We investigate in this paper three indexing techniques for users’ subscriptions based on inverted lists or on an ordered trie for exact and partial matching. We present analytical models for memory requirements and matching time and we conduct a thorough experimental evaluation to exhibit the impact of critical parameters of realistic web syndication workloads.

Multimedia News Summarization in Search

It is a necessary but challenging task to relieve users from the proliferative news information and allow them to quickly and comprehensively master the information of the whats and hows that are happening in the world every day. In this article, we develop a novel approach of multimedia news summarization for searching results on the Internet, which uncovers the underlying topics among query-related news information and threads the news events within each topic to generate a query-related brief overview. First, the hierarchical latent Dirichlet allocation (hLDA) model is introduced to discover the hierarchical topic structure from query-related news documents, and a new approach based on the weighted aggregation and max pooling is proposed to identify one representative news article for each topic. One representative image is also selected to visualize each topic as a complement to the text information. Given the representative documents selected for each topic, a time-bias maximum spanning tree (MST) algorithm is proposed to thread them into a coherent and compact summary of their parent topic. Finally, we design a friendly interface to present users with the hierarchical summarization of their required news information. Extensive experiments conducted on a large-scale news dataset collected from multiple news Web sites demonstrate the encouraging performance of the proposed solution for news summarization in news retrieval.

Fast Gradient Descent for Drifting Least Squares Regression, with Application to Bandits

Online learning algorithms require to often recompute least squares regression estimates of parameters. We study improving the computational complexity of such algorithms by using stochastic gradient descent (SGD) type schemes in place of classic regression solvers. We show that SGD schemes efficiently track the true solutions of the regression problems, even in the presence of a drift. This finding coupled with an $O(d)$ improvement in complexity, where $d$ is the dimension of the data, make them attractive for implementation in the \textit{big data} settings. In the case when strong convexity in the regression problem is guaranteed, we provide bounds on the error both in expectation and high probability (the latter is often needed to provide theoretical guarantees for higher level algorithms), despite the drifting least squares solution. As an example of this case we prove that the regret performance of an SGD version of the PEGE linear bandit algorithm is worse than that of PEGE itself only by a factor of $O(\log^4 n)$. When strong convexity of the regression problem cannot be guaranteed, we investigate using an adaptive regularisation. We make an empirical study of an adaptively regularised, SGD version of LinUCB in a news article recommendation application, which uses the large scale news recommendation dataset from Yahoo! front page. These experiments show a large gain in computational complexity and a consistently low tracking error.

Report on the analyses and the applications of a large-scale news video archive: NII TV-RECS

Report on the analyses and the applications of a large-scale news video archive: NII TV-RECS

Enhancing news organization for convenient retrieval and browsing

To facilitate users to access news quickly and comprehensively, we design a news search and browsing system named GeoVisNews, in which the news elements of “Where”, “Who”, “What” and “When” are enhanced via news geo-localization, image enrichment and joint ranking, respectively. For news geo-localization, an Ordinal Correlation Consistent Matrix Factorization (OCCMF) model is proposed to maintain the relevance rankings of locations to a specific news document and simultaneously capture intra-relations among locations and documents. To visualize news, we develop a novel method to enrich news documents with appropriate web images. Specifically, multiple queries are first generated from news documents for image search, and then the appropriate images are selected from the collected web images by an intelligent fusion approach based on multiple features. Obtaining the geo-localized and image enriched news resources, we further employ a joint ranking strategy to provide relevant, timely and popular news items as the answer of user searching queries. Extensive experiments on a large-scale news dataset collected from the web demonstrate the superior performance of the proposed approaches over related methods.

Cross-modality based celebrity face naming for news image collections

For automatically mining the underlying relationships between different famous persons in daily news, for example, building a news person based network with the faces as icons to facilitate face-based person finding, we need a tool to automatically label faces in new images with their real names. This paper studies the problem of linking names with faces from large-scale news images with captions. In our previous work, we proposed a method called Person-based Subset Clustering which is mainly based on face clustering for all face images derived from the same name. The location where a name appears in a caption, as well as the visual structural information within a news image provided informative cues such as who are really in the associated image. By combining the domain knowledge from the captions and the corresponding image we propose a novel cross-modality approach to further improve the performance of linking names with faces. The experiments are performed on the data sets including approximately half a million news images from Yahoo! news, and the results show that the proposed method achieves significant improvement over the clustering-only methods.

Machiavellian Democracy in the Good Society

Machiavellian Democracy in the Good Society

Efficient Tracking of News Topics Based on Chronological Semantic Structures in a Large-Scale News Video Archive

Recent advance in digital storage technology has enabled us to archive a large volume of video data. Thanks to this trend, we have archived more than 1,800 hours of video data from a daily Japanese news show in the last ten years. When considering the effective use of such a large news video archive, we assumed that analysis of its chronological and semantic structure becomes important. We also consider that providing the users with the development of news topics is more important to help their understanding of current affairs, rather than providing a list of relevant news stories as in most of the current news video retrieval systems. Therefore, in this paper, we propose a structuring method for a news video archive, together with an interface that visualizes the structure, so that users could track the development of news topics according to their interest, efficiently. The proposed news video structure, namely the “topic thread structure”, is obtained as a result of an analysis of the chronological and semantic relation between news stories. Meanwhile, the proposed interface, namely “mediaWalker II”, allows users to track the development of news topics along the topic thread structure, and at the same time watch the video footage corresponding to each news story. Analyses on the topic thread structures obtained by applying the proposed method to actual news video footages revealed interesting and comprehensible relations between news topics in the real world. At the same time, analyses on their size quantified the efficiency of tracking a user's topic-of-interest based on the proposed topic thread structure. We consider this as a first step towards facilitating video authoring by users based on existing contents in a large-scale news video archive.

Integrating multi-modal content analysis and hyperbolic visualization for large-scale news video retrieval and exploration

In this paper, we have developed a novel scheme to achieve more effective analysis, retrieval and exploration of large-scale news video collections by performing multi-modal video content analysis and synchronization. First, automatic keyword extraction is performed on news closed captions and audio channels to detect the most interesting news topics (i.e., keywords for news topic interpretation), and the associations among these news topics (i.e., contextual relationships among the news topics) are further determined according to their co-occurrence probabilities. Second, visual semantic items, such as human faces, text captions, video concepts, are extracted automatically by using our semantic video analysis techniques. The news topics are automatically synchronized with the most relevant visual semantic items. In addition, an interestingness weight is assigned for each news topic to characterize its importance. Finally, a novel hyperbolic visualization scheme is incorporated to visualize large-scale news topics according to their associations and interestingness. With a better global overview of large-scale news video collections, users can specify their queries more precisely and explore large-scale news video collections interactively. Our experiments on large-scale news video collections have provided very positive results.