EDBT 2026 Demo / reviewers in the wild / expert
Adam St. Arnaud
dblp:139/9131
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author
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.
| Artificial intelligence
1 paper |
Information extraction and text analysis · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › multilingual NLP
cognate identification |
0.3 | 1 | 2017 | Identifying Cognate Sets Across Dictionaries of Related Languages · EMNLP 2017 |
Computational social science and digital humanities
historical linguistics |
0.1 | 1 | 2017 | Identifying Cognate Sets Across Dictionaries of Related Languages · EMNLP 2017 |
Methods — techniques the papers use, named apart from their topics
semantic similarity · 0.6phonetic similarity · 0.6clustering · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Identifying Cognate Sets Across Dictionaries of Related LanguagesabstractWe present a system for identifying cognate sets across dictionaries of related languages.The likelihood of a cognate relationship is calculated on the basis of a rich set of features that capture both phonetic and semantic similarity, as well as the presence of regular sound correspondences.The similarity scores are used to cluster words from different languages that may originate from a common protoword.When tested on the Algonquian language family, our system detects 63% of cognate sets while maintaining cluster purity of 70%. Adam St. Arnaud, David Beck, Grzegorz Kondrak |
EMNLP | 1 |