Search Results for author: Sussi Olsen

Found 16 papers, 2 papers with code

An empirically grounded expansion of the supersense inventory

1 code implementation GWC 2016 Hector Martinez Alonso, Anders Johannsen, Sanni Nimb, Sussi Olsen, Bolette Pedersen

We cover the expansion of the already-established supernsense inventory for nouns and verbs, the addition of coarse supersenses for adjectives in absence of a canonical supersense inventory, and super-senses for verbal satellites.

Compiling a Suitable Level of Sense Granularity in a Lexicon for AI Purposes: The Open Source COR Lexicon

no code implementations LREC 2022 Bolette Pedersen, Nathalie Carmen Hau Sørensen, Sanni Nimb, Ida Flørke, Sussi Olsen, Thomas Troelsgård

We present The Central Word Register for Danish (COR), which is an open source lexicon project for general AI purposes funded and initiated by the Danish Agency for Digitisation as part of an AI initiative embarked by the Danish Government in 2020.

Merging DanNet with Princeton Wordnet

no code implementations GWC 2019 Bolette Sandford Pedersen, Sanni Nimb, Ida Rørmann Olsen, Sussi Olsen

Since the Danish wordnet is built bottom-up from Danish lexica and corpora, all taxonomies are monolingually based and thus not necessarily directly compatible with the coverage and structure of the Princeton WordNet.

DanNet2: Extending the coverage of adjectives in DanNet based on thesaurus data (project presentation)

no code implementations EACL (GWC) 2021 Sanni Nimb, Bolette Pedersen, Sussi Olsen

We describe the methodology and initial work of semi-automatically transferring adjectives from the Danish Thesaurus to the wordnet with the aim of easily enlarging the coverage from 3, 000 to approx.

Towards a principled approach to sense clustering – a case study of wordnet and dictionary senses in Danish

no code implementations GWC 2018 Bolette Pedersen, Manex Agirrezabal, Sanni Nimb, Ida Olsen, Sussi Olsen

Our aim is to develop principled methods for sense clustering which can make existing lexical resources practically useful in NLP – not too fine-grained to be operational and yet finegrained enough to be worth the trouble.

Clustering

A Thesaurus-based Sentiment Lexicon for Danish: The Danish Sentiment Lexicon

no code implementations LREC 2022 Sanni Nimb, Sussi Olsen, Bolette Pedersen, Thomas Troelsgård

The annotation task of the dataset included several steps, and was based on the comparison of synonyms and near synonyms within a semantic field.

LEMMA

The SemDaX Corpus ― Sense Annotations with Scalable Sense Inventories

no code implementations LREC 2016 Bolette Pedersen, Anna Braasch, Anders Johannsen, H{\'e}ctor Mart{\'\i}nez Alonso, Sanni Nimb, Sussi Olsen, Anders S{\o}gaard, Nicolai Hartvig S{\o}rensen

The aim of the developed corpus is twofold: i) to assess the reliability of the different sense annotation schemes for Danish measured by qualitative analyses and annotation agreement scores, and ii) to serve as training and test data for machine learning algorithms with the practical purpose of developing sense taggers for Danish.

Providing a Catalogue of Language Resources for Commercial Users

no code implementations LREC 2016 Bente Maegaard, Lina Henriksen, Andrew Joscelyne, Vesna Lusicky, Margaretha Mazura, Sussi Olsen, Claus Povlsen, Philippe Wacker

The Language Technology Observatory will provide easy access to information about LRs deemed to be useful for MT and other translation tools through its LR Catalogue.

Machine Translation Translation

Using TEI, CMDI and ISOcat in CLARIN-DK

no code implementations LREC 2014 Dorte Haltrup Hansen, Lene Offersgaard, Sussi Olsen

This paper presents the challenges and issues encountered in the conversion of TEI header metadata into the CMDI format.

A Distributed Resource Repository for Cloud-Based Machine Translation

no code implementations LREC 2012 J{\"o}rg Tiedemann, Dorte Haltrup Hansen, Lene Offersgaard, Sussi Olsen, Matthias Zumpe

In this paper, we present the architecture of a distributed resource repository developed for collecting training data for building customized statistical machine translation systems.

Machine Translation Management +2

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