Papers by Michael Rosner
Cross-Lingual Link Discovery for Under-Resourced Languages (2022.lrec-1)
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Michael Rosner, Sina Ahmadi, Elena-Simona Apostol, Julia Bosque-Gil, Christian Chiarcos, Milan Dojchinovski, Katerina Gkirtzou, Jorge Gracia, Dagmar Gromann, Chaya Liebeskind, Giedrė Valūnaitė Oleškevičienė, Gilles Sérasset, Ciprian-Octavian Truică
| Challenge: | Linked data paradigms can be used to solve under-resourced languages' problem of under-utilization of resources. |
| Approach: | They propose a paradigm for cross-lingual link discovery that can be applied to under-resourced languages . they argue that techniques for cross language linking can be readily applied . |
| Outcome: | The proposed technologies can be applied to under-resourced languages, the authors argue . the authors show that the Linked Data paradigm can be used to solve the problem . |
From Linguistic Linked Data to Big Data (2024.lrec-main)
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Dimitar Trajanov, Elena Apostol, Radovan Garabik, Katerina Gkirtzou, Dagmar Gromann, Chaya Liebeskind, Cosimo Palma, Michael Rosner, Alexia Sampri, Gilles Sérasset, Blerina Spahiu, Ciprian-Octavian Truică, Giedre Valunaite Oleskeviciene
| Challenge: | Language data on the LOD cloud has grown in number, size, and variety . Linked (Open) Data (LLOD) is a standardized way of representing and sharing linguistic datasets . |
| Approach: | They propose to combine LLOD and Big Data to improve interoperability of linguistic datasets . they propose to use a machine-readable format to represent and share linguistic data . |
| Outcome: | This paper examines the use cases of Linked (Open) Data and Big Data in language data. |
Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions (L18-1)
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Albert Gatt, Marc Tanti, Adrian Muscat, Patrizia Paggio, Reuben A Farrugia, Claudia Borg, Kenneth P Camilleri, Michael Rosner, Lonneke van der Plas
| Challenge: | a crowdsourcing study has been conducted to generate rich textual descriptions of human faces . the aim is to investigate how users describe images of human face images . |
| Approach: | They propose to extend the problem of automatically generating text from images to face description . they conducted an annotation study on a subset of the corpus to gain a better understanding of the variation they find in face descriptions . |
| Outcome: | The proposed corpus is based on images taken in the wild and is expected to be large enough to support non-trivial machine learning work on the automated description of faces. |