Papers by Sussi Olsen
A Thesaurus-based Sentiment Lexicon for Danish: The Danish Sentiment Lexicon (2022.lrec-1)
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| Challenge: | a newly published Danish sentiment lexicon with a high lexical coverage was compiled using lexicographic methods and linked data. |
| Approach: | They propose to use lexicographic methods to compile a Danish sentiment lexicon with a high lexical coverage by linking words from a thesaurus to a comprehensive monolingual dictionary. |
| Outcome: | The proposed lexicon contains 13,859 Danish polarity lemmas and includes morphological information. |
Compiling a Suitable Level of Sense Granularity in a Lexicon for AI Purposes: The Open Source COR Lexicon (2022.lrec-1)
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Bolette Pedersen, Nathalie Carmen Hau Sørensen, Sanni Nimb, Ida Flørke, Sussi Olsen, Thomas Troelsgård
| Challenge: | The central word register for Danish is an open source lexicon project for general AI purposes funded and initiated by the Danish Agency for Digitisation in 2020. |
| Approach: | They propose to use existing fine-grained sense inventory to compile a more AI-appropriate sense granularity level of the vocabulary. |
| Outcome: | The proposed lexical resource is based on the fine-grained sense inventory from Den Danske Ordbog (DDO) it is designed to be more practical and suitable for AI, omitting outdated language and slang, merging subtle and rare sub-senses with their main sense, disregarding sub-domains, etc. |
A Danish FrameNet Lexicon and an Annotated Corpus Used for Training and Evaluating a Semantic Frame Classifier (L18-1)
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| Challenge: | a Danish FrameNet is a lexicon based on the Danish Thesaurus . it is significantly faster than building a new one from scratch . |
| Approach: | They propose a way to efficiently compile a Danish FrameNet based on the Danish Thesaurus . they present the corresponding corpus annotations of frames and roles and show how this can be used for a semantic frame classifier . |
| Outcome: | The proposed approach is faster than building a lexicon from scratch. |
A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment (2020.lrec-1)
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Sina Ahmadi, John Philip McCrae, Sanni Nimb, Fahad Khan, Monica Monachini, Bolette Pedersen, Thierry Declerck, Tanja Wissik, Andrea Bellandi, Irene Pisani, Thomas Troelsgård, Sussi Olsen, Simon Krek, Veronika Lipp, Tamás Váradi, László Simon, András Gyorffy, Carole Tiberius, Tanneke Schoonheim, Yifat Ben Moshe, Maya Rudich, Raya Abu Ahmad, Dorielle Lonke, Kira Kovalenko, Margit Langemets, Jelena Kallas, Oksana Dereza, Theodorus Fransen, David Cillessen, David Lindemann, Mikel Alonso, Ana Salgado, José Luis Sancho, Rafael-J. Ureña-Ruiz, Jordi Porta Zamorano, Kiril Simov, Petya Osenova, Zara Kancheva, Ivaylo Radev, Ranka Stanković, Andrej Perdih, Dejan Gabrovsek
| Challenge: | a new dataset aims to align monolingual dictionaries with a single sense level for 15 languages . this dataset covers a wide range of languages and resources . |
| Approach: | They propose to manually align monolingual dictionaries with possible semantic relationships . they use 15 languages to create a new baseline for the task of monolingual word sense alignment . |
| Outcome: | The proposed dataset covers 15 languages and covers the more challenging task of linking general-purpose language. |
Towards a Danish Semantic Reasoning Benchmark - Compiled from Lexical-Semantic Resources for Assessing Selected Language Understanding Capabilities of Large Language Models (2024.lrec-main)
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| Challenge: | a semantic reasoning benchmark for Danish is compiled from human-curated lexical-semantic resources. |
| Approach: | They present a semantic reasoning benchmark for Danish compiled semi-automatically from a number of human-curated lexical-semantic resources. |
| Outcome: | The proposed datasets are compiled semi-automatically from human-curated lexical-semantic resources. |