Ilya Tikhomirov

Relational-situational method for text search and analysis and its applications

A relational-situational method for analysis of natural language texts is outlined based on the theory of communicative grammar of the Russian language and the theory of heterogeneous semantic networks. It is shown that the relational-situational method can be used for precise search of documents in local and globalnets and electronic libraries

Application of linguistic knowledge to search precision improvement

The paper presents methods for semantically relevant search. The authors mainly focus on the usage of linguistic approach to improvement of search precision in search engines. The effectiveness of the presented approach is proved by the experiments with a search engine.

Technologies for semantic analysis of scientific publications

The paper presents technologies for semantic analysis of scientific publications. We mainly focus on the stages of natural languages processing and analysis results. The results of experiments with scientific publication examining are presented.

Exactus Like: Plagiarism Detection in Scientific Texts

The paper presents an overview of Exactus Like – a plagiarism detection system. Deep parsing for text alignment helps the system to find moderate forms of disguised plagiarism. The features of the system and its advantages are discussed. We describe the architecture of the system and present its performance.

Information Retrieval for R&D Support

Research and development (R&D) involves not only researchers but also many other specialists from different areas. All of them solve a variety of tasks that require comprehensive information and analytical support. This chapter discusses the major tasks arising in R&D: study of the state of the art in a given research area, prospects assessment of research fields and forecasting their development, quality assessment of scientific publications including plagiarism detection, and automated examination of proposed R&D projects. A number of informational and analytical systems have been developed to address these tasks. The main goal of this chapter is to give a review of R&D support functions of well-known and widely-used search and analytical systems and discuss information retrieval methods behind these functions.

Social Tension Detection and Intention Recognition Using Natural Language Semantic Analysis: On the Material of Russian-Speaking Social Networks and Web Forums

The paper proposes a method of social tension detection and intention recognition based on natural language analysis of social networks, forums, blogs and news comments. Our approach combines natural language syntax and semantics analysis with statistical processing to identify possible indicators of social tension. The universal components of our method incorporate the general laws of natural language, general psychological, sociological and psycholinguistic rules and trends typical of social tension detection in virtual discussions. Automatic monitoring of the contents of discussions helps to timely unveil hidden signs of tension and makes it possible to predict the likely development of the situation.

Method for Pornography Filtering in the WEB Based onAutomatic Classification and Natural Language Processing

The paper presents a method for pornography detection in the web pages based on natural language processing. The described classification method uses feature set of single words and groups of words. Syntax analysis is performed to extract collocations. A modification of TF-IDF is used to weight terms. An evaluation and comparison of quality and performance of classification are given.

Training Datasets Collection and Evaluation of Feature Selection Methods for Web Content Filtering

This paper focuses on the main aspects of development of a qualitative system for dynamic content filtering. These aspects include collection of meaningful training data and the feature selection techniques. The Web changes rapidly so the classifier needs to be regularly re-trained. The problem of training data collection is treated as a special case of the focused crawling. A simple and easy-to-tune technique was proposed, implemented and tested. The proposed feature selection technique tends to minimize the feature set size without loss of accuracy and to consider interlinked nature of the Web. This is essential to make a content filtering solution fast and non-burdensome for end users, especially when content filtering is performed using a restricted hardware. Evaluation and comparison of various classifiers and techniques are provided.

Automatic Image Classification for Web Content Filtering: New Dataset Evaluation

The paper presents experimental evaluation of image classification in the field of web content filtering using bag of visual features and convolutional neural networks approach. A more difficult data set than traditionally used ones was built from very similar types of images in order to make conditions closer to real world practice. F1-measure of classifiers that are based on bags of visual features was significantly lower than that reported in previously published papers. Convolutional neural networks performed much better. Also, we measured and compared training and prediction time of various algorithms.

Scientific Research Funding Criteria: An Empirical Study of Peer Review and Scientometrics

In this paper we investigated the problem of scientific research funding from the perspective of data-mining. The object was to conduct versatile retrospective analysis of decisions made by the Russian Foundation for Basic Research regarding scientific research funding. The central task of the analysis was to compare the impact of various items of information on final decision making. In other words, we tried to answer two questions: (a) what does an evaluation committee mainly look at when it selects projects for funding; (b) are scientometric indicators (or science metrics) useful in decision analysis? To achieve this, we built predictive models (classifiers), performed introspection (extracted feature importance) and compared them. The input data was a set of review forms (questionnaires) from the Russian Foundation for Basic Research completed in by peer reviewers. Final decision is made by the foundation board (an evaluation committee). Finally, we concluded that the available input (project proposals, expert assessments and scientometric data) was not enough to explain all the decisions. We showed that scientometric data does not have any significant influence on project proposals assessment. It also means that h-index, mean impact factor, publication and citation number cannot supersede the peer review procedure.