Search Results for author: Ophélie Lacroix

Found 6 papers, 1 papers with code

Resources and Evaluations for Danish Entity Resolution

no code implementations CRAC (ACL) 2021 Maria Barrett, Hieu Lam, Martin Wu, Ophélie Lacroix, Barbara Plank, Anders Søgaard

Automatic coreference resolution is understudied in Danish even though most of the Danish Dependency Treebank (Buch-Kromann, 2003) is annotated with coreference relations.

coreference-resolution Entity Disambiguation +2

DDisCo: A Discourse Coherence Dataset for Danish

no code implementations LREC 2022 Linea Flansmose Mikkelsen, Oliver Kinch, Anders Jess Pedersen, Ophélie Lacroix

We thus present DDisCo, a dataset including text from the Danish Wikipedia and Reddit annotated for discourse coherence.

DaNLP: An open-source toolkit for Danish Natural Language Processing

no code implementations NoDaLiDa 2021 Amalie Brogaard Pauli, Maria Barrett, Ophélie Lacroix, Rasmus Hvingelby

We present an open-source toolkit for Danish Natural Language Processing, enabling easy access to Danish NLP’s latest advancements.

Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses

no code implementations EMNLP 2020 Simon Flachs, Ophélie Lacroix, Helen Yannakoudakis, Marek Rei, Anders Søgaard

Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum of GEC applications.

Grammatical Error Correction Language Modelling

Weakly Supervised POS Taggers Perform Poorly on Truly Low-Resource Languages

no code implementations28 Apr 2020 Katharina Kann, Ophélie Lacroix, Anders Søgaard

Part-of-speech (POS) taggers for low-resource languages which are exclusively based on various forms of weak supervision - e. g., cross-lingual transfer, type-level supervision, or a combination thereof - have been reported to perform almost as well as supervised ones.

Cross-Lingual Transfer POS +1

Cannot find the paper you are looking for? You can Submit a new open access paper.