Papers by Krister Lindén
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe (2020.lrec-1)
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Georg Rehm, Katrin Marheinecke, Stefanie Hegele, Stelios Piperidis, Kalina Bontcheva, Jan Hajič, Khalid Choukri, Andrejs Vasiļjevs, Gerhard Backfried, Christoph Prinz, José Manuel Gómez-Pérez, Luc Meertens, Paul Lukowicz, Josef van Genabith, Andrea Lösch, Philipp Slusallek, Morten Irgens, Patrick Gatellier, Joachim Köhler, Laure Le Bars, Dimitra Anastasiou, Albina Auksoriūtė, Núria Bel, António Branco, Gerhard Budin, Walter Daelemans, Koenraad De Smedt, Radovan Garabík, Maria Gavriilidou, Dagmar Gromann, Svetla Koeva, Simon Krek, Cvetana Krstev, Krister Lindén, Bernardo Magnini, Jan Odijk, Maciej Ogrodniczuk, Eiríkur Rögnvaldsson, Mike Rosner, Bolette Pedersen, Inguna Skadiņa, Marko Tadić, Dan Tufiș, Tamás Váradi, Kadri Vider, Andy Way, François Yvon
| Challenge: | Language Technologies (LTs) are a powerful means to break down language barriers impacting business, cross-lingual and cross-cultural communication in Europe. |
| Approach: | They present an overview of the European LT landscape and the current state of play in industry and the LT market. |
| Outcome: | The present study outlines funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. |
HeLI-OTS, Off-the-shelf Language Identifier for Text (2022.lrec-1)
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| Challenge: | Existing off-the-shelf language identification tools favor widely used languages, but Heli-OTS can be used to identify a large group of languages. |
| Approach: | They introduce an off-the-shelf text language identification tool using the HeLI method . they compare the He LI-OTS language identifier with fastText on two different data sets . |
| Outcome: | The proposed language identification tool is compared with fastText on two different data sets. |
BabyFST - Towards a Finite-State Based Computational Model of Ancient Babylonian (2020.lrec-1)
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| Challenge: | morphological analyzer for Akkadian is not yet available for the extinct language . we present a general finite-state based model for Babylonian that can achieve a coverage of 97.3% and a recall of 93.7% on token level. |
| Approach: | They propose a general finite-state based morphological model for Babylonian that can achieve a coverage of 97.3% and recall up to 93.7% on lemmatization and POS-tagging tasks. |
| Outcome: | The proposed model can achieve coverage and recall of 97.3% on lemmatization and POS-tagging tasks on token level from a transcribed input. |
Automated Phonological Transcription of Akkadian Cuneiform Text (2020.lrec-1)
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| Challenge: | Akkadian was an east-semitic language spoken in ancient Mesopotamia . cuneiform text does not mark the inflection for logograms, so the inflected form needs to be inferred from the sentence context. |
| Approach: | They propose to automate phonological transcription of the transliterated Akkadian corpora . transcription is normalized according to the grammatical description of a given dialect . they find that cuneiform text does not mark the inflection for logograms . |
| Outcome: | The proposed transcriptions show the Akkadian renderings for Sumerian logograms, while the logogram transcription is more challenging. |