Demo Data from EDBT 2026 / reviewers in the wild / expert
Arto Nordlund
dblp:205/8975
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Health and well-being technologies · 100% | |
| Artificial intelligence
1 paper |
Information extraction and text analysis · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Medical and health informatics › clinical diagnosis › neurodegenerative disease diagnosis
mild cognitive impairment detection |
0.3 | 1 | 2017 | An analysis of eye-movements during reading for the detection of mild cognitive impairment · EMNLP 2017 |
Natural language and speech › Information extraction and text analysis
feature engineering |
0.1 | 1 | 2017 | An analysis of eye-movements during reading for the detection of mild cognitive impairment · EMNLP 2017 |
Methods — techniques the papers use, named apart from their topics
machine learning · 0.9eye-tracking features · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | A Swedish Cookie-Theft Corpus
Dimitrios Kokkinakis, Kristina Lundholm Fors, Kathleen C. Fraser, Arto Nordlund |
LREC | 4 |
| 2017 | An analysis of eye-movements during reading for the detection of mild cognitive impairmentabstractWe present a machine learning analysis of eye-tracking data for the detection of mild cognitive impairment, a decline in cognitive abilities that is associated with an increased risk of developing dementia.We compare two experimental configurations (reading aloud versus reading silently), as well as two methods of combining information from the two trials (concatenation and merging).Additionally, we annotate the words being read with information about their frequency and syntactic category, and use these annotations to generate new features.Ultimately, we are able to distinguish between participants with and without cognitive impairment with up to 86% accuracy. Kathleen C. Fraser, Kristina Lundholm Fors, Dimitrios Kokkinakis, Arto Nordlund |
EMNLP | 4 |