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Some thoughts on using annotated suffix trees for Natural Language Processing
P. 5–18.
Artemova E.
The paper defines an annotated suffix tree (AST) - a data structure used to calculate and store the frequencies of all the fragments of the given string or a collection of strings. The AST is associated with a string to text scoring, which takes all fuzzy matches into account. We show how the AST and the AST scoring can be used for Natural Language Processing tasks. Copyright © by the paper's authors. Copying only for private and academic purposes.
In book
Issue 1410. , Aachen: CEUR-WS, 2015.
Pletnev Sergey, , in: Crowd Science Workshop: Trust, Ethics, and Excellence in Crowdsourced Data Management at Scale (CSW 2021).: Copenhagen, Denmark: CEUR Workshop Proceedings, 2021. Ch. 1 P. 15–20.
Most speech-driven systems on the first step convert audio to text through an automatic speech recognition (ASR) model and then pass the text to any downstream natural language processing (NLP) modules. However, these ASR models can lead to system failure or undesirable output when being exposed to natural language perturbation or variation in practice. In ...
Added: December 13, 2021
Copenhagen, Denmark: CEUR Workshop Proceedings, 2021.
The second workshop on Crowd Science is organized in conjunction with the 47th International Conference on Very Large Data Bases (VLDB 2021). This workshop is the second in a series of events that has the goal of helping crowdsourcing “transition” from art to science, and tackles the research challenges that we face to make crowdsourcing ...
Added: December 13, 2021
Mirkin B., Frolov D., Vlasov A. et al., , in: Intelligent Data Engineering and Automated Learning – IDEAL 2020/ 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part IIVol. 12490: Lecture Notes in Computer Science.: Cham: Springer, 2020. P. 423–433.
We define and find a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a taxonomy. This generalization lifts the set to a “head subject” in the higher ranks of the taxonomy, that is supposed to “tightly” cover the query set, possibly bringing in some errors, both ...
Added: November 13, 2020
Gharavi E., Veisi H., Россо П., Neural Computing and Applications 2020 Vol. 32 No. 14 P. 10593–10607
The efficiency and scalability of plagiarism detection systems have become a major challenge due to the vast amount of available textual data in several languages over the Internet. Plagiarism occurs in different levels of obfuscation, ranging from the exact copy of original materials to text summarization. Consequently, designed algorithms to detect plagiarism should be robust ...
Added: October 29, 2020
Mirkin B., Frolov D., Vlasov A. et al., , in: WIMS 2020: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics.: Association for Computing Machinery (ACM), 2020. P. 184–189.
We define and find a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a taxonomy. This generalization lifts the set to a “head subject” in the higher ranks of the taxonomy, that is supposed to “tightly” cover the query set, possibly bringing in some errors, both ...
Added: August 28, 2020
Frolov D., Mirkin B., Nascimento S. et al., , in: Intelligent Data Engineering and Automated Learning – IDEAL 2019Vol. 2.: Springer, 2019. P. 3–11.
We define a most specific generalization of a fuzzy set of
topics assigned to leaves of the rooted tree of a domain taxonomy. This
generalization lifts the set to its “head subject” node in the higher ranks
of the taxonomy tree. The head subject is supposed to “tightly” cover
the query set, possibly bringing in some errors referred to ...
Added: December 7, 2019
Springer, 2019.
We define a most specific generalization of a fuzzy set of
topics assigned to leaves of the rooted tree of a domain taxonomy. This
generalization lifts the set to its “head subject” node in the higher ranks
of the taxonomy tree. The head subject is supposed to “tightly” cover
the query set, possibly bringing in some errors referred to ...
Added: December 7, 2019
Pimonova E., Durandin O., Malafeev A., , in: Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Lecture Notes in Computer Science, Revised Selected PapersVol. 11832.: Cham: Springer, 2019. P. 193–204.
This work tackles the problem of modeling author style in Russian. In particular, we solve the task of authorship attribution using the collected dataset of 30 authors, 1506 texts written in the period of 18th – 21st century. We apply various approaches to solving the attribution problem: Random Forest, Logistic Regression, SVM Classifier. In terms ...
Added: November 7, 2019
Frolov D., Mirkin B., Nascimento S. et al., , in: Fuzzy Systems (FUZZ-IEEE), IEEE International Conference Proceedings.: IEEE, 2019. P. 1–6.
This paper presents an algorithm, ParGenFS, for generalizing, or “lifting”, a fuzzy set of topics to higher ranks of a hierarchical taxonomy of a research domain. The algorithm ParGenFS finds a globally optimal generalization of the topic set to minimize a penalty function, by balancing the number of introduced “head subjects” and related errors, the ...
Added: October 30, 2019
Frolov D., Nascimento S., Fenner T. et al., Information Sciences 2020 Vol. 512 P. 595–615
This paper proposes a novel method, referred to as ParGenFS, for finding a most specific generalization of a query set represented by a fuzzy set of topics assigned to leaves of the rooted tree of a taxonomy. The query set is generalized by “lifting” it to one or more “head subjects” in the higher ranks ...
Added: October 9, 2019
Frolov D., Mirkin B., Nascimento S. et al., , in: Optimization of Complex Systems: Theory, Models, Algorithms and Applications.: Switzerland: Springer Publishing Company, 2020. P. 779–789.
This paper presents a relatively rare case of an optimization problem in data analysis to admit a globally optimal solution by a recursive algorithm. We are concerned with finding a most specific generalization of a fuzzy set of topics assigned to leaves of domain taxonomy represented by a rooted tree. The idea is to “lift” ...
Added: June 25, 2019
International Academy, Research, and Industry Association (IARIA), 2019.
Added: June 4, 2019
Frolov D., Mirkin B., Nascimento S. et al., , in: International Conference on Artificial Intelligence and Soft Computing. 18th International Conference, ICAISC 2019, Zakopane, Poland, June 16–20, 2019, Proceedings* 1. Issue 11508.: Cham: Springer, 2019. P. 273–286.
We define and find a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a taxonomy. This generalization lifts the set to a “head subject” in the higher ranks of the taxonomy, that is supposed to “tightly” cover the query set, possibly bringing in some errors, both ...
Added: June 3, 2019
Cham: Springer, 2019.
The series Lecture Notes in Computer Science (LNCS), including its subseries Lecture Notes in Artificial Intelligence (LNAI) and Lecture Notes in Bioinformatics (LNBI), has established itself as a medium for the publication of new developments in computer science and information technology research and teaching - quickly, informally, and at a high level.
The two-volume set LNCS ...
Added: June 3, 2019
Voznesenskaya T., Леднов Д. А., Машинное обучение и анализ данных 2018 Т. 4 № 4 С. 266–279
This paper is toward the system of automatic text summarization developed by «DC – Systems» company in cooperation with the faculty of computer science at HSE. The summary is a concise description of the text in terms of its content and meaning, i.e. from the point of view of its semantics. The purpose of the ...
Added: October 5, 2018
Shishkova A., Artemova E., , in: CLLS 2016. Computational Linguistics and Language Science. Proceedings of the Workshop on Computational Linguistics and Language Science. Moscow, Russia, April 26, 2016Vol. 1886.: Aachen: CEUR Workshop Proceedings, 2017. P. 42–47.
The paper presents an unsupervised and knowledge-free ap- proach to compound splitting. Although the research is focused on Ger- man compounds, the method is expected to be extensible to other com- pounding languages. The approach is based on the annotated suffix tree (AST) method proposed and modified by Mirkin et al. To the best of ...
Added: October 10, 2017
Artemova E., , in: Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing.: Stroudsburg, PA: Association for Computational Linguistics, 2017. P. 97–101.
In this paper we address the problem of filtering obscene lexis in Russian texts. We use string similarity measures to find words similar or identical to words from a stop list and establish both a test collec- tion and a baseline for the task. Our exper- iments show that a novel string similarity measure based ...
Added: October 10, 2017
Artemova E., Ilvovsky D., , in: The 3d International Workshop on Concept Discovery in Unstructured Data (CDUD 2016). Proceedings of the Third Workshop on Concept Discovery in Unstructured Data co-located with the 13th International Conference on Concept Lattices and Their Applications (CLA 2016), Moscow, Russia, July 18, 2016. CEUR Workshop ProceedingsVol. 1625.: Aachen: CEUR Workshop Proceedings, 2016. P. 25–31.
In this paper an extension of tf-idf weighting on annotated suffix tree (AST) structure is described. The new weighting scheme can be used for computing similarity between texts, which can further serve as in input to clustering algorithm. We present preliminary tests of us-ing AST for computing similarity of Russian texts and show slight im-provement ...
Added: October 26, 2016
Artemova E., Mirkin B., Annals of Data Science 2015 Vol. 2 No. 1 P. 61–82
A step-by-step approach to taxonomy construction is presented. On the first step, the upper layer frame of taxonomy is built manually according to educational materials. On the next steps, the frame is refined at a chosen topic using the Wikipedia category tree and articles, both cleaned of noise. Our main tool in this is a ...
Added: May 27, 2015
Artemova E., , in: Proceedings of The Eighth International Conference on Web Search and Data Mining.: NY, United States of America: ACM, 2014. Ch. 58 P. 429–434.
An approach to multiple labeling research papers is explored. We develop techniques for annotating/labeling research pa- pers in informatics and computer sciences with key phrases taken from the ACM Computing Classification System. The techniques utilize a phrase-to-text relevance measure so that only those phrases that are most relevant go to the anno- tation. Three phrase-to-text ...
Added: December 8, 2014