News Retrieval System Research Articles

Decentralized News-Retrieval Architecture Using Blockchain Technology

Trust is a critical element when it comes to news articles, and an important problem is how to ensure trust in the published information on news websites. First, this paper describes the inner workings of a proposed news-retrieval and aggregation architecture employed by a blockchain-based solution for fighting disinformation; this includes a comparison between existing information retrieval solutions. The decentralized nature of the solution is achieved by separating the crawling (i.e., extracting the web page links) from the scraping (i.e., extracting the article information) and having third-party actors extract the data. A majority-rule mechanism is used to determine the correctness of the information, and the blockchain network is used for traceability. Second, the steps needed to deploy the distributed components in a cloud environment seamlessly are discussed in detail, with a special focus on the open-source OpenStack cloud solution. Lastly, novel methods for achieving a truly decentralized architecture based on community input and blockchain technology are presented, thus ensuring maximum trust and transparency in the system. The results obtained by testing the proposed news-retrieval system are presented, and the optimizations that can be made are discussed based on the crawling and scraping test results.

Design and Implementation of Tourism News Information Retrieval System using Modified Cosine Similarity

Technological developments cannot be denied a very meaningful impact on human life so that what was previously done traditionally is now completely digital, for example conventional news media which has been transformed into an online news portal so that it can still reach its readers. Online news portals provide a lot of up-to-date information, including tourism news, which is an industry that continues to grow and has the opportunity to create new jobs for the community. Currently, to search for tourism news, people only need to type a keyword (query) in the search engine which will then display the latest news about tourism. However, not all tourism news displayed matches what they are looking for, so readers have to re-check which takes a lot of time. Therefore, a tourism news retrieval system that can display the most relevant tourism news to the query is proposed. The Modified Cosine method shows good results in document clustering to bring the inter-cluster distance closer. This study uses the Modified Cosine method and TF-IDF weighting schema to determine the value of precision, recall, and f-measure in calculating the similarity of the query to tourism news documents. The system has been tested using 3 types of queries, with 5 different words each. The test results show that Modified Cosine method obtained best precision value in the test using 5 words in the query and the F-Measure value and the best recall on the 3 words test in query.

Automatic identification of focus personage in multi-lingual news images

Annotations of character IDs in news images are critical as ground truth for news retrieval and recommendation system. Universality and accuracy optimization of deep neural network models constitutes the key technology to improve the precision and computing efficiency of automatic news character identification, which is attracting increased attention globally. This paper explores the optimized deep neural network model for automatic focus personage identification in multi-lingual news. First, the face model of the focus personage is trained by using the corresponding face images from German news as positive samples. Next, the scheme of Recurrent Convolutional Neural Network (RCNN) + Bi-directional Long-Short Term Memory (Bi-LSTM) + Conditional Random Field (CRF) is utilized to label the focus name, and the RCNN-RCNN encoder–decoder is applied to translate names of people into multiple languages. Third, face features are described by combining the advantages of Local Gabor Binary Pattern Histogram Sequence (LGBPHS) and RCNN, and iterative quantization (ITQ) is used to binarize codes. Finally, a name semantic network is built for different domains. Experiments are performed on a dataset which comprises approximately 100,000 news images. The experimental results demonstrate that the proposed method achieves a significant improvement over other algorithms.

Face naming in news images via multiple instance learning and hybrid recurrent convolutional neural network

Annotations of subject IDs in images are very important as ground truth for face recognition applications and news retrieval systems. Face naming is becoming a significant research topic in news image indexing applications. By exploiting the uniqueness of name, face naming is transformed to the problem of multiple instance learning (MIL) with exclusive constraint, namely the eMIL problem. First, the positive bags and the negative bags are automatically annotated by a hybrid recurrent convolutional neural network and a distributed affinity propagation cluster. Next, positive instance selection and updating are used to reduce the influence of false-positive bag and to improve the performance. Finally, max exclusive density and iterative Max-ED algorithms are proposed to solve the eMIL problem. The experimental results show that the proposed algorithms achieve a significant improvement over other algorithms.

Cloudpress 2.0: a new-age news retrieval system on the cloud

In this era of the internet, a huge amount of news articles are added every minute of everyday. As a result of this evergrowing amount of news articles, news retrieval systems are required to process the news articles frequently and intensively. The news retrieval systems that are in use today barely cope up with these data-intensive computations. Cloudpress 2.0 presented here, is designed and implemented to be scalable, robust and fault tolerant. It is designed in such a way that all the processes involved in news retrieval, such as fetching, text processing, image processing, indexing, storing and summarising, exploit MapReduce paradigm and use the power of the cloud computing. It uses novel approaches for parallel processing, for storing the news articles in a distributed database and for visualising them as a 3D visual. It uses Lucene-based indexing for efficient and faster retrieval. It also includes a novel query expansion feature for searching the news articles. Cloudpress 2.0 also allows on-the-fly, extractive summarisation of news articles based on the input query.

Investigating User Perception in a Development of Crime News Retrieval System

This paper outlines our user requirement study, to investigate user perception from the end-user on the development of a crime news retrieval system. The objective of the study is to obtain more insight on user perception and opinion on the development of a crime news retrieval system. We administered a survey to a group of 34 respondents using a questionnaire. The findings showed a positive feedback with overall higher percentage of user preferences. We identified several possible design such as visualization and searching element to be incorporated in the prototype interfaces. The work highlight several potential design elements to be incorporated into the future crime news retrieval system development.

Development of an intelligent distributed news retrieval system

Currently available web news retrieval systems face a number of problems in that web-based news retrieval requires the ability to quickly and accurately process and update a very large amount of data which are constantly being updated. In this paper, we present the development of an intelligent distributed web news retrieval system the goal of which is to accurately retrieve and organize the web news information. It includes: a novel optimized crawler algorithm whose fetching-speed is several times faster than that of the traditional crawler; a keen tag based extraction algorithm which can extract the data rich content with minimal manual effort and which also allows data to be classified as important or not important so that the crawler can revisit and update important data; a modified MapReduce improved by estimating the execution time of each subtask, which is proven to be able to reduce the number of the unusual tasks and shorten the whole job execution time.

Supporting Sense-making with Tools for Structuring a Concept Space: A Proposal for Design and Evaluation

This paper describes a research proposal to investigate sense-making processes in complex situations with the assistance of information systems. It presents the design of a sense-making tool to be integrated with a news retrieval system. The proposed user study aims to understand how users use this tool to establish and organize their conceptual models of a network of concepts and relationships.

NowOnWeb: a NewsIR System

Nowadays there are thousands of news sites available on-line. Traditional methods to access this huge news repository are overwhelmed. In this paper we present NowOnWeb, a news retrieval system that crawls the articles from the internet publishers and provides news searching and browsing.

The SoVideo Mandarin Chinese Broadcast News Retrieval System

This paper describes the SoVideo broadcast news retrieval system for Mandarin Chinese. The system is based on technologies such as large vocabulary continuous speech recognition for Mandarin Chinese, automatic story segmentation, and information retrieval. Currently, the database consists of 177 hours of broadcast news, which yielded 3,264 stories by automatic story segmentation. We discuss the development and evaluation of each component of the retrieval system.

Harmful effects of seemingly helpful information on forecasts of stock earnings : Fred D. Davis (University of Minnesota), Gerald L. Lohse (University of Pennsylvania), and Jeffrey E. Kottemann (Salisbury State University)


 
 
 Decision makers have expanding access to business information via computerized news retrieval systems. A greater understanding is needed about how this news retrieval informa- tion influences their performance and confidence. MBA students from an advanced finance course forecasted stock earnings using a computerized information system designed to simulate systems used in practice. Disguised actual company data were presented in three different treatments: baseline information, baseline plus redundant news information, and baseline plus nonredundant news information. The redundant information made subjects significantly more confident in their forecasts compared to the baseline case. The nonredun- dam information made subjects significantly more confident than both the baseline case and the redundant case. Forecast accuracy, however, was significantly diminished in both the redundant and nonredundant conditions compared to baseline. Thus, the additional news information, whether redundant or nonredundant, had the effect of degrading performance while increasing confidence. This indicates that decision makers may be poor judges of the usefulness of newly available information sources, and may be influenced by information that does not improve their performance under the false impression that it is helpful. Journal of Economic Psychology, June 1994, 15(2): 253-267. (Reprinted with permission of North Holland Publishing Company.)
 
 

Harmful effects of seemingly helpful information on forecasts of stock earnings

Decision makers have expanding access to business information via computerized news retrieval systems. A greater understanding is needed about how this news retrieval information influences their performance and confidence. MBA students from an advanced finance course forecasted stock earnings using a computerized information system designed to simulate systems used in practice. Disguised actual company data were presented in three different treatments: baseline information, baseline plus redundant news information, and baseline plus nonredundant news information. The redundant information made subjects significantly more confident in their forecasts compared to the baseline case. The nonredundant information made subjects significantly more confident than both the baseline case and the redundant case. Forecast accuracy, however, was significantly diminished in both the redundant and nonredundant conditions compared to baseline. Thus, the additional news information, whether redundant or nonredundant, had the effect of degrading performance while increasing confidence. This indicates that decision makers may be poor judges of the usefulness of newly available information sources, and may be influenced by information that does not improve their performance under the false impression that it is helpful.

An On-line News Retrieval System "Nikkei Recall" by the Nihon Keizai Shimbun

An On-line News Retrieval System "Nikkei Recall" by the Nihon Keizai Shimbun