Work

An Algorithm of Finding Thematically Similar Documents with Creating Context-semantic Graph Based on Probabilistic-entropy Approach

Year: 2015

Type: conference-paper

Abstract: An algorithm of finding documents on a given topic based on a selected reference collection of documents along with creating context-semantic graph for visualizing themes in search results is presented. The algorithm is based on integration of set of probabilistic, entropic, and semantic markers for extractions of weighted key words and combinations of words, which describe the given topic. Test results demonstrate an average precision of 99% and the recall of 84% on expert selection of documents. Also developed special approach to constructing graph on base of algorithms that extract key phrases with weights. It gives the possibility to demonstrate a structure of subtopics in large collections of documents in compact graph form.

Source: Procedia Computer Science

DOI: 10.1016/j.procs.2015.11.035

Authors: I.A. Moloshnikov, A.G. Sboev, R.B. Rybka, D.V. Gydovskikh

Institutions: Kurchatov Institute, National Research Nuclear University MEPhI

Language: English

Topic: Advanced Text Analysis Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Russian Foundation for Basic Research

Awards: G2615622630, G6065839518