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Christopher Jenkins 0001

dblp:17/3809-1 · also Chris Jenkins 0001 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation › text representation
contextualized word embeddings
0.712023
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
0.712023
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
0.212023
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
YearPublicationVenuePosition
2023 To Split or Not to Split: Composing Compounds in Contextual Vector Spaces
abstract
We 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
EMNLP1