News

3 papers by CopeNLU authors are accepted to appear at NAACL 2019. Topics span from population of typological knowledge bases and weak supervision from disparate lexica to frame detection in online fora.

35 Marie Skłodowska-Curie PhD fellowships are available at the University of Copenhagen Faculty of Science via the TALENT program. The fellowship offers applicants employment as PhD fellows for a period of three years on a research topic of the fellow’s choice. The current call is open from 15 February to 1 April 2019 for a start date in autumn 2019.

People

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Assistant Professor

Isabelle’s main research interests are natural language understanding and learning with limited training data.

Postdoc

Johannes researches multi-lingual and multi-task learning, with a particular focus on exploiting similarities between languages.

PhD Student

Pepa’s research interests are multilingual fact checking and question answering.

PhD Student

This is a new PhD student, joining in Spring 2019.

PhD Intern

Giannis is a PhD Student at Ghent University, and is visiting CopeNLU in Spring 2019 to work on joint information extraction.

PhD Intern

Luna is a PhD Student at Ghent University, and is visiting CopeNLU in Spring 2019 to work on emotion detection.

PhD Intern

Farhad is a PhD Student at the University of Oslo, and is visiting CopeNLU in Spring 2019 to work on domain adaptation for information extraction.

Lecturer

This is Ryan. He’s a lecturer at the University of Cambridge and a frequent collaborator of the CopeNLU group.

PhD Student

Yova researches low-resource and cross-lingual learning. She’s a member of the CoAStaL NLP group and co-advised by Isabelle.

PhD Student

Ana’s main research interest is question answering, with a particular focus on the customer support domain. She’s a member of the CoAStaL NLP group and co-advised by Isabelle.

PhD Student

Mareike is interested in fact checking and multi-lingual learning. She’s a member of the CoAStaL NLP group and co-advised by Isabelle.

Research Assistant

Now a lead data scientist at Archii

Recent Publications

More Publications

In the Principles and Parameters framework, the structural features of languages depend on parameters that may be toggled on or off, …

When assigning quantitative labels to a dataset, different methodologies may rely on different scales. One such example is sentiment …

In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects …

A neural language model trained on a text corpus can be used to induce distributed representations of words, such that similar words …

Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In …

The best systems at the SemEval-16 and SemEval-17 community question answering shared tasks – a task that amounts to question …

Previous work has suggested that parameter sharing between transition-based neural dependency parsers for related languages can lead to …

This paper documents the Team Copenhagen system which placed first in the CoNLL–SIGMORPHON 2018 shared task on universal …

Punctuation is a strong indicator of syntactic structure, and parsers trained on text with punctuation often rely heavily on this …

Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions. For …

Recent Posts

The University of Copenhagen is a great place if you’re both interested in high-quality NLP research and a high quality of life.

Projects

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Knowledge Base Population

Extract information about entities, phrases and relations between them from text to populate knowledge bases

Learning with Limited Labelled Data

Learning with limited labelled data, including multi-task learning, weakly supervised and zero-shot learning

Multilingual Learning

Training models to work well for multiple languages, including low-resource ones

Question Answering

Answer questions automatically, including in conversational settings

Stance Detection and Fact Checking

Determine the attitude expressed in a text towards a topic, and use this for automatic evidence-based fact checking

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