Construction of query concepts based on feature clustering of documents

In Information Retrieval, since it is hard to identify users’ information needs, many approaches have been tried to solve this problem by expanding initial queries and reweighting the terms in the expanded queries using users’ relevance judgments. Although relevance feedback is most effective when relevance information about retrieved documents is provided by users, it is not always available. Another solution is to use correlated terms for query expansion. The main problem with this approach is how to construct the term-term correlations that can be used effectively to improve retrieval performance. In this study, we try to construct query concepts that denote users’ information needs from a document space, rather than to reformulate initial queries using the term correlations and/or users’ relevance feedback. To form query concepts, we extract features from each document, and then cluster the features into primitive concepts that are then used to form query concepts. Experiments are performed on the Associated Press (AP) dataset taken from the TREC collection. The experimental evaluation shows that our proposed framework called QCM (Query Concept Method) outperforms baseline probabilistic retrieval model on TREC retrieval.     

Analysis of Statistical Question Classification for Fact-Based Questions

Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.     

Russian Science Citation Index: Areas for Development Identified by Active Users

The current state of the Russian Science Citation Index is analyzed. Several areas for its further development identified by long-standing active users, such as data searching, refinement, and storage; current user alerts; data input with the Science Index license; patent information; and the application programming interface (API) are investigated.     

Explaining Media Choice: The Role of Issue-Specific Engagement in Predicting Interest-Based and Partisan Selectivity

This study analyzes the predictors of 2 types of media selectivity: interest-based (i.e., choice of entertainment over politics) and partisan (i.e., choice of pro-attitudinal over counter-attitudinal or balanced news). Relying on a large survey-based experiment, we find that issue-specific engagement variables, including perceived issue understanding, issue importance, and issue attitude strength, predict interest-based and partisan selectivity above and beyond the influence of general political knowledge, news interest, and strength of political leanings. These results show that the drivers of selectivity are more complex than general political attributes; rather, they are contextual and reflect people’s engagement with particular issues.     

On the training of highly qualified scientific staff in the field of information security

This article considers the dynamics and specifics of training highly qualified scientific staff (philosophy doctors and doctors of sciences) in the field of information security and data protection in a 10-year period of 2004–2013 in basic research specialties as classified by the Higher Attestation Commission (VAC) of the Ministry of Education and Science of the Russian Federation.     

Symbolic Management of the Degree of Information Security by Integer Splitting

This paper demonstrates the application of module arithmetic for controlling the level of information security by a new technique referred as integer splitting. The basic definitions and concepts of the method are provided. The mathematical function of arising transformation is described in detail; its properties were studied and the basic theorems were proven that justify the use of splitting in practical applications. An active system was designed and characteristic cases of its operation are considered.