First Advisor

William Comer

Date of Award

Summer 2020

Document Type


Degree Name

Bachelor of Science (B.S.) in Computer Science and University Honors


Computer Science




Natural language processing (Computer science), Computational linguistics, Information storage and retrieval systems, Scholarly periodicals -- Russia -- Analysis




The automatic extraction of keyphrases from scholarly papers is a necessary step for many Natural Language Processing (NLP) tasks, including text retrieval, machine translation, and text summarization. However, due to the different grammatical and semantic intricacies of languages, this is a highly language-dependent task. Many free and open source implementations of state-of-the-art keyphrase extraction techniques exist, but they are not adapted for processing Russian text. Furthermore, the multi-linguistic character of scholarly papers in the field of Russian computational linguistics and NLP introduces additional complexity to keyphrase extraction. This paper describes a free and open source program as a proof of concept for a topic-clustering approach to the automatic extraction of keyphrases from the largest conference on Russian computational linguistics and intellectual technologies, Dialogue. The goal of this paper is to use LDA and pyLDAvis to discover the latent topics of the Dialogue conference and to extract the salient keyphrases used by the research community. The conclusion points to needed improvements to techniques for PDF text extraction, morphological normalization, and candidate keyphrase ranking.


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The code used in this paper is free and open source, and can be accessed through GitHub.

Persistent Identifier