Nikita Astrakhantsev

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The conceptualization of knowledge required for an efficient processing of textual data is usually represented as ontologies. Depending on the knowledge domain and tasks, different types of ontologies are constructed: formal ontologies, which involve axioms and detailed relations between concepts; taxonomies, which are hierarchically organized concepts; and(More)
This paper presents an experimental evaluation of the state-of-the-art approaches for automatic term recognition based on multiple features: machine learning method and voting algorithm. We show that in most cases machine learning approach obtains the best results and needs little data for training; we also find the best subsets of all popular features.
A framework for fast text analysis, which is developed as a part of the Texterra project, is described. Texterra provides a scalable solution for the fast text processing on the basis of novel methods that exploit knowledge extracted from the Web and text documents. For the developed tools, details of the project, use cases, and evaluation results are(More)
Applications related to domain specific text processing often use glossaries and ontologies, and the main step of such resource construction is term recognition. This paper presents a survey of existing definitions of the term and its linguistic features, formulates the task definition for term recognition, and analyzes presently-available methods for(More)
This paper describes a novel approach to automate extraction of useful information from tables and to record the knowledge procured in a structured data repository. The approach is based on modeling a behavior of an expert, who collects tabular data and maps them to a predefined relational schema. Experimental results demonstrate that the proposed approach(More)
Automatically recognized terminology is widely used for various domain-specific texts processing tasks, such as machine translation, information retrieval or ontology construction. However, there is still no agreement on which methods are best suited for particular settings and, moreover, there is no reliable comparison of already developed methods. We(More)
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