no code implementations • 9 Feb 2024 • Neslihan Suzen, Evgeny M. Mirkes, Damian Roland, Jeremy Levesley, Alexander N. Gorban, Tim J. Coats
Electronic patient records (EPRs) produce a wealth of data but contain significant missing information.
no code implementations • 31 May 2022 • Neslihan Suzen, Alexander N. Gorban, Jeremy Levesley, Evgeny M. Mirkes
This paper introduces computational methods for semantic analysis and the quantifying the meaning of short scientific texts.
no code implementations • 26 Apr 2021 • Neslihan Suzen, Alexander Gorban, Jeremy Levesley, Evgeny Mirkes
We show that an informational approach to representing the meaning of a text has offered a way to effectively predict the scientific impact of research papers.
no code implementations • 18 Sep 2020 • Neslihan Suzen, Alexander Gorban, Jeremy Levesley, Evgeny Mirkes
In this paper we argue that (lexical) meaning in science can be represented in a 13 dimension Meaning Space.
no code implementations • 28 Apr 2020 • Neslihan Suzen, Evgeny M. Mirkes, Alexander N. Gorban
The LSC is a scientific corpus of 1, 673, 350 abstracts and the LScDC is a scientific dictionary which words are extracted from the LSC.
1 code implementation • 14 Dec 2019 • Neslihan Suzen, Evgeny M. Mirkes, Alexander N. Gorban
In this paper, we present a scientific corpus of abstracts of academic papers in English -- Leicester Scientific Corpus (LSC).
2 code implementations • 27 Jul 2018 • Neslihan Suzen, Alexander Gorban, Jeremy Levesley, Evgeny Mirkes
The main novelty in this paper is that we design a model to predict marks based on the similarities between the student answers and the model answer.