Demo Data from EDBT 2026 / reviewers in the wild / expert
Christopher Jenkins 0001
dblp:17/3809-1 · also Chris Jenkins 0001
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021
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 |
Language models and text generation · 87% Information extraction and text analysis · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › text representation
contextualized word embeddings |
0.7 | 1 | 2023 | To Split or Not to Split: Composing Compounds in Contextual Vector Spaces · EMNLP 2023 |
Natural language and speech › Language models and text generation › tokenization
subword tokenization |
0.7 | 1 | 2023 | To Split or Not to Split: Composing Compounds in Contextual Vector Spaces · EMNLP 2023 |
Natural language and speech › Information extraction and text analysis › lexical semantics
compositionality prediction |
0.2 | 1 | 2023 | To Split or Not to Split: Composing Compounds in Contextual Vector Spaces · EMNLP 2023 |
Methods — techniques the papers use, named apart from their topics
masked language modeling · 0.7BERT · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | To Split or Not to Split: Composing Compounds in Contextual Vector SpacesabstractWe investigate the effect of sub-word tokenization on representations of German noun compounds: single orthographic words which are composed of two or more constituents but often tokenized into units that are not morphologically motivated or meaningful.Using variants of BERT models and tokenization strategies on domain-specific restricted diachronic data, we introduce a suite of evaluations relying on the masked language modelling task and compositionality prediction.We obtain the most consistent improvements by pre-splitting compounds into constituents. Christopher Jenkins 0001, Filip Miletic 0002, Sabine Schulte im Walde |
EMNLP | 1 |