Zobrazeno 1 - 10
of 339
pro vyhledávání: '"Trajanov, A."'
Lexicon-based sentiment analysis (SA) in finance leverages specialized, manually annotated lexicons created by human experts to extract sentiment from financial texts. Although lexicon-based methods are simple to implement and fast to operate on text
Externí odkaz:
http://arxiv.org/abs/2306.03997
The growing trend of Large Language Models (LLM) development has attracted significant attention, with models for various applications emerging consistently. However, the combined application of Large Language Models with semantic technologies for re
Externí odkaz:
http://arxiv.org/abs/2305.04676
Autor:
Rizinski, Maryan, Jankov, Andrej, Sankaradas, Vignesh, Pinsky, Eugene, Miskovski, Igor, Trajanov, Dimitar
In recent years, natural language processing (NLP) has become increasingly important in a variety of business applications, including sentiment analysis, text classification, and named entity recognition. In this paper, we propose an approach for com
Externí odkaz:
http://arxiv.org/abs/2305.01028
Autor:
Trajanov, Risto, Nikolikj, Ana, Cenikj, Gjorgjina, Teytaud, Fabien, Videau, Mathurin, Teytaud, Olivier, Eftimov, Tome, López-Ibáñez, Manuel, Doerr, Carola
Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, max
Externí odkaz:
http://arxiv.org/abs/2209.04412
Autor:
Trajanov, Dimitar, Trajkovski, Vangel, Dimitrieva, Makedonka, Dobreva, Jovana, Jovanovik, Milos, Klemen, Matej, Žagar, Aleš, Robnik-Šikonja, Marko
Natural language processing (NLP) is an area of artificial intelligence that applies information technologies to process the human language, understand it to a certain degree, and use it in various applications. This area has rapidly developed in the
Externí odkaz:
http://arxiv.org/abs/2208.10228
Publikováno v:
IEEE Access, Vol 12, Pp 7170-7198 (2024)
Lexicon-based sentiment analysis in finance leverages specialized, manually annotated lexicons created by human experts to extract sentiment from financial texts effectively. Although lexicon-based methods are simple to implement and fast to operate
Externí odkaz:
https://doaj.org/article/638e705920e24d07a7db8c87dc4d69c2
Predicting the performance of an optimization algorithm on a new problem instance is crucial in order to select the most appropriate algorithm for solving that problem instance. For this purpose, recent studies learn a supervised machine learning (ML
Externí odkaz:
http://arxiv.org/abs/2203.11828
Efficient solving of an unseen optimization problem is related to appropriate selection of an optimization algorithm and its hyper-parameters. For this purpose, automated algorithm performance prediction should be performed that in most commonly-appl
Externí odkaz:
http://arxiv.org/abs/2110.11633
Publikováno v:
Computers. 2023; 12(1):17
The challenge of recognizing named entities in a given text has been a very dynamic field in recent years. This is due to the advances in neural network architectures, increase of computing power and the availability of diverse labeled datasets, whic
Externí odkaz:
http://arxiv.org/abs/2102.13139
Autor:
Christian Chiarcos, Purificação Silvano, Mariana Damova, Giedre Valunaite Oleškeviciene, Chaya Liebeskind, Dimitar Trajanov, Ciprian-Octavian Truică, Elena-Simona Apostol, Anna Bączkowska
Publikováno v:
Rasprave Instituta za Hrvatski Jezik i Jezikoslovlje, Vol 49, Iss 1, Pp 117-136 (2023)
Linguistic Linked Open Data (LLOD) are technologies that provide a powerful instrument for representing and interpreting language phenomena on a web-scale. The main objective of this paper is to demonstrate how LLOD technologies can be applied to rep
Externí odkaz:
https://doaj.org/article/d5d36c8e8a2c4b998ddf3c23cedb1f5a