Computational linguistics and intelligent systems. – 2019 р.

Permanent URI for this collectionhttps://ena.lpnu.ua/handle/ntb/45481

Періодичне видання за матеріалами конференції

This volume represents the proceedings of the Workshop Conference, with Posters and Demonstrations track, of the 3rd International Conference on Computational Linguistics and Intelligent Systems, held in Kharkiv, Ukraine, in April 2019. It comprises 13 contributed papers that were carefully peer-reviewed and selected from 27 submissions. The volume opens with the abstracts of the keynote talks. The rest of the collection is organized in two parts. Parts II contain the contributions to the Main COLINS Conference tracks, structured in two topical sections: (I) Computational Linguistics; (II) Intelligent Systems.

Computational Linguistics and Intelligent Systems. – Lviv : Lviv Politechnic Publishing House, 2019. – Volume 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18–19, 2019. – 78 p.

Computational Linguistics and Intelligent Systems

Зміст (том 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019)

1. Титульний аркуш до "Computational Linguistics and Intelligent Systems"	1
2. Зміст до "Computational Linguistics and Intelligent Systems"	12
3. Berko A. Knowledge-based Big Data Cleanup method	14
4. Grabar N., Hamon T. WikiWars-UA: Ukrainian corpus annotated with temporal expressions	22
5. Klyushin D., Lyashko S., Zub S. A(n) Assumption in machine learning	32
6. Puzik O. Intelligence knowledge-based system based on multilingual dictionaries	39
7. Bilova M., Trehubenko O. Study of software systems usability used for customers loyalty identification	46
8. Bobrovnyk K. Automated building and analysis of Ukrainian Twitter corpus for toxic text detection	55
9. Chuiko V., Khairova N. Semantic similarity identification for short text fragments	57
10. Drobot T. Data-to-text generation for domain-specific purposes	60
11. Liutenko I., Kurasov O. Consideration of the software tests quality evaluation problem	62
12. Lytvynenko J. Identify of the substantive, attribute, and verb collocations in russian text	66
13. Manuilov I., Petrasova S. Method for paraphrase extractionfrom the news text corpus	69
14. Razno M. Machine learning text classification model with NLP approach	71
15. Shanidze O., Petrasova S. Extraction of semantic relations from Wikipedia text corpus	74
16. Author index, Reviewers	76

Content (Vol. 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019)

1. Titulnii arkush do "Computational Linguistics and Intelligent Systems"	1
2. Zmist do "Computational Linguistics and Intelligent Systems"	12
PAPER PRESENTATIONS
3. Berko A. Knowledge-based Big Data Cleanup method	14
4. Grabar N., Hamon T. WikiWars-UA: Ukrainian corpus annotated with temporal expressions	22
5. Klyushin D., Lyashko S., Zub S. A(n) Assumption in machine learning	32
6. Puzik O. Intelligence knowledge-based system based on multilingual dictionaries	39
STUDENT SECTION
7. Bilova M., Trehubenko O. Study of software systems usability used for customers loyalty identification	46
8. Bobrovnyk K. Automated building and analysis of Ukrainian Twitter corpus for toxic text detection	55
9. Chuiko V., Khairova N. Semantic similarity identification for short text fragments	57
10. Drobot T. Data-to-text generation for domain-specific purposes	60
11. Liutenko I., Kurasov O. Consideration of the software tests quality evaluation problem	62
12. Lytvynenko J. Identify of the substantive, attribute, and verb collocations in russian text	66
13. Manuilov I., Petrasova S. Method for paraphrase extractionfrom the news text corpus	69
14. Razno M. Machine learning text classification model with NLP approach	71
15. Shanidze O., Petrasova S. Extraction of semantic relations from Wikipedia text corpus	74
16. Author index, Reviewers	76

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A(n) Assumption in machine learning
(Lviv Politechnic Publishing House, 2019-04-18) Klyushin, Dmitry; Lyashko, Sergey; Zub, Stanislav; Taras Shevchenko National University of Kyiv
The commonly used statistical tools in machine learning are two-sample tests for verifying hypotheses on homogeneity, for example, for estimation of corpushomogeneity, testing text authorship and so on. Often, they are effective only for sufficiently large sample (n> 100) and have limited application in situations where the size of samples is small (n < 30). To solve the problem for small samples, methods of reproducing samples are often used: jackknife and bootstrap. We propose and investigate a family of homogeneity measures based on A(n) assumption that are effective both for small and large samples.