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
12
14
22
32
39
46
55
57
60
62
66
69
71
74
76

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


1
12
PAPER PRESENTATIONS
14
22
32
39
STUDENT SECTION
46
55
57
60
62
66
69
71
74
76

Browse

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Automated building and analysis of Ukrainian Twitter corpus for toxic text detection
    (Lviv Politechnic Publishing House, 2019-04-18) Bobrovnyk, Kateryna; Taras Shevchenko National University of Kyiv
    Toxic text detection is an emerging area of study in Inter-net linguistics and corpus linguistics. The relevance of the topic can be explained by the lack of Ukrainian social media text corpora that are publicly available. Research involves building of the Ukrainian Twitter corpus by means of scraping; collective annotation of 'toxic/non-toxic' texts; construction of the obscene words dictionary for future feature engineering; and models training for the task of text classi cation (com-paring Logistic Regression, Support Vector Machine, and Deep Neural Network).