Measuring multimedia content proximity via artificial intelligence methods

Rybalka, A. I.; Megel, Y. E.; Mikhnova, O.

Публікація:
Measuring multimedia content proximity via artificial intelligence methods

dc.contributor.author	Rybalka, A. I.
dc.contributor.author	Megel, Y. E.
dc.contributor.author	Mikhnova, O.
dc.date.accessioned	2021-03-04T20:08:17Z
dc.date.available	2021-03-04T20:08:17Z
dc.date.issued	2020
dc.description.abstract	The paper describes existing and novel approaches to word sequence similarity measuring which uses image processing procedures to reveal proximity relations between words and their meaning. This is implemented due to the fact the each word sequence can be associated with a particular pictorial domain, and thus any textual information can be potentially presented via an image series. Theoretical results of rule based approach, genetic algorithms and neural networks application are analyzed in terms of solving the problem under study. The proposed information processing technology may be useful for automatic translation applications, known as CAT-tools by now, search engine optimization, etc.	uk_UA
dc.identifier.citation	Rybalka A. I. Measuring multimedia content proximity via artificial intelligence methods / Rybalka A. I., Megel Y. E., Mikhnova O. //Metrology and metrology assurance: Works of 30th International Scientific Symposium September 7 -11, 2020, Sozopol, Bulgaria.	uk_UA
dc.identifier.uri	http://openarchive.nure.ua/handle/document/14772
dc.language.iso	en	uk_UA
dc.publisher	Technical University of Sofia	uk_UA
dc.subject	computer-assisted translation	uk_UA
dc.subject	evolutionary approach	uk_UA
dc.subject	multimedia processing	uk_UA
dc.subject	neural network	uk_UA
dc.title	Measuring multimedia content proximity via artificial intelligence methods	uk_UA
dc.type	Conference proceedings	uk_UA
dspace.entity.type	Publication