Papers by Chaya Liebeskind
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 . |
Automatic Thesaurus Construction for Modern Hebrew (L18-1)
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| Challenge: | Modern Hebrew lacks lexical resources fundamental to many natural language processing tools. |
| Approach: | They propose a method for generating a cooccurrence based thesaurus in a MRL and a distributional similarity method for Hebrew. |
| Outcome: | The proposed method is not optimal for modern Hebrew, the authors show . they used Hebrew WordNet as their gold standard for the analysis . |
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. |
MultiLexBATS: Multilingual Dataset of Lexical Semantic Relations (2024.lrec-main)
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Dagmar Gromann, Hugo Goncalo Oliveira, Lucia Pitarch, Elena-Simona Apostol, Jordi Bernad, Eliot Bytyçi, Chiara Cantone, Sara Carvalho, Francesca Frontini, Radovan Garabik, Jorge Gracia, Letizia Granata, Fahad Khan, Timotej Knez, Penny Labropoulou, Chaya Liebeskind, Maria Pia Di Buono, Ana Ostroški Anić, Sigita Rackevičienė, Ricardo Rodrigues, Gilles Sérasset, Linas Selmistraitis, Mahammadou Sidibé, Purificação Silvano, Blerina Spahiu, Enriketa Sogutlu, Ranka Stanković, Ciprian-Octavian Truică, Giedre Valunaite Oleskeviciene, Slavko Zitnik, Katerina Zdravkova
| Challenge: | Prior work has focused on analysing lexical semantic relations in word embeddings or probing pretrained language models (PLMs) with some exceptions. |
| Approach: | They propose to use a multilingual parallel dataset of lexical semantic relations adapted from BATS in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian as an experiment on cross-lingual transfer of relational knowledge. |
| Outcome: | The proposed dataset is adapted from a BATS-based dataset in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian. |
Morphological Complexity of Children Narratives in Eight Languages (2022.lrec-1)
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Gordana Hržica, Chaya Liebeskind, Kristina Š. Despot, Olga Dontcheva-Navratilova, Laura Kamandulytė-Merfeldienė, Sara Košutar, Matea Kramarić, Giedrė Valūnaitė Oleškevičienė
| Challenge: | morphological complexity of a corpus representing the language production of younger and older children is compared across different languages. |
| Approach: | a study compares morphological complexity of a corpus representing language production of younger and older children across different languages. |
| Outcome: | The results show that younger children corpora have lower morphological complexity than older children corpus for Spanish and Russian. |
Offensive language detection in Hebrew: can other languages help? (2022.lrec-1)
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| Challenge: | Various approaches for offensive language detection have been applied for this task . contamination of social networks with offensive content is a new reality affecting almost all of us . |
| Approach: | They propose to use multiple supervised models and text representations to detect offensive language in three languages, including two Semitic languages. |
| Outcome: | The proposed model can detect offensive content in two Semitic languages, including Hebrew and Arabic, and it is able to perform cross-lingual and multilingual learning. |
ISO-based Annotated Multilingual Parallel Corpus for Discourse Markers (2022.lrec-1)
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Purificação Silvano, Mariana Damova, Giedrė Valūnaitė Oleškevičienė, Chaya Liebeskind, Christian Chiarcos, Dimitar Trajanov, Ciprian-Octavian Truică, Elena-Simona Apostol, Anna Baczkowska
| Challenge: | Discourse markers carry information about the discourse structure and organization, and also signal local dependencies or epistemic stance of speaker. |
| Approach: | They propose an ISO-based annotated multilingual parallel corpus for discourse markers . they propose an annotation scheme for discourse relations with a plug-in to ISO 24617-2 . |
| Outcome: | The proposed language resource is based on an ISO-based annotated multilingual parallel corpus of discourse markers. |