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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 42 1 EMBEDDIA tools output example corpus of Estonian, Croatian and Latvian news articles 1.0 Freienthal, Linda; Pelicon, Andraž; Martinc, Matej. - : Ekspress Meedia Group, 2022. : Styria Media Group, 2022 BASE Show details 2 Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised? ... Koloski, Boshko; Pollak, Senja; Škrlj, Blaž. - : arXiv, 2022 BASE Show details 3 Word-embedding based bilingual terminology alignment ... Repar, Andraž; Martinc, Matej; Ulčar, Matej. - : Zenodo, 2021 BASE Show details 4 Word-embedding based bilingual terminology alignment ... Repar, Andraž; Martinc, Matej; Ulčar, Matej. - : Zenodo, 2021 BASE Show details 5 Ekspress news article archive (in Estonian and Russian) 1.0 Purver, Matthew; Pollak, Senja; Freienthal, Linda. - : Ekspress Meedia Group, 2021 BASE Show details 6 Latvian user comment dataset 1.0 Shekhar, Ravi; Purver, Matthew; Pollak, Senja. - : Ekspress Meedia Group, 2021 BASE Show details 7 Ekspress user comment dataset 1.0 Shekhar, Ravi; Pollak, Senja; Pelicon, Andraž. - : Ekspress Meedia Group, 2021 BASE Show details 8 24sata news comment dataset 1.0 Shekhar, Ravi; Pranjic, Marko; Pollak, Senja. - : Styria Media Group, 2021 BASE Show details 9 Keyword extraction datasets for Croatian, Estonian, Latvian and Russian 1.0 Koloski, Boshko; Pollak, Senja; Škrlj, Blaž. - : Ekspress Meedia Group, 2021. : Styria Media Group, 2021 BASE Show details 10 24sata news article archive 1.0 Purver, Matthew; Shekhar, Ravi; Pranjić, Marko. - : Styria Media Group, 2021 BASE Show details 11 Latvian Delfi article archive (in Latvian and Russian) 1.0 Pollak, Senja; Purver, Matthew; Shekhar, Ravi. - : Ekspress Meedia Group, 2021 BASE Show details 12 List of single-word male and female occupations in Slovenian Supej, Anka; Ulčar, Matej; Robnik-Šikonja, Marko. - : Jožef Stefan Institute, 2021. : Faculty of Computer and Information Science, University of Ljubljana, 2021 BASE Show details 13 SimLex-999 Slovenian translation SimLex-999-sl 1.0 Pollak, Senja; Vulić, Ivan; Pelicon, Andraž. - : University of Ljubljana, 2021 BASE Show details 14 Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic languages ... Piskorski, Jakub; Babych, Bogdan; Kancheva, Zara. - : Zenodo, 2021 BASE Show details 15 Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic languages ... Piskorski, Jakub; Babych, Bogdan; Kancheva, Zara. - : Zenodo, 2021 BASE Show details 16 Evaluation of contextual embeddings on less-resourced languages ... Ulčar, Matej; Žagar, Aleš; Armendariz, Carlos S.. - : arXiv, 2021 BASE Show details 17 Simple Discovery of COVID IS WAR Metaphors Using Word Embeddings ... Brglez, Mojca; Pollak, Senja; Vintar, Špela. - : Zenodo, 2021 BASE Show details 18 Simple Discovery of COVID IS WAR Metaphors Using Word Embeddings ... Brglez, Mojca; Pollak, Senja; Vintar, Špela. - : Zenodo, 2021 BASE Show details 19 Investigating cross-lingual training for offensive language detection Pelicon, Andraž; Shekhar, Ravi; Škrlj, Blaž; Purver, Matthew; Pollak, Senja In: PeerJ Comput Sci (2021) Abstract: Platforms that feature user-generated content (social media, online forums, newspaper comment sections etc.) have to detect and filter offensive speech within large, fast-changing datasets. While many automatic methods have been proposed and achieve good accuracies, most of these focus on the English language, and are hard to apply directly to languages in which few labeled datasets exist. Recent work has therefore investigated the use of cross-lingual transfer learning to solve this problem, training a model in a well-resourced language and transferring to a less-resourced target language; but performance has so far been significantly less impressive. In this paper, we investigate the reasons for this performance drop, via a systematic comparison of pre-trained models and intermediate training regimes on five different languages. We show that using a better pre-trained language model results in a large gain in overall performance and in zero-shot transfer, and that intermediate training on other languages is effective when little target-language data is available. We then use multiple analyses of classifier confidence and language model vocabulary to shed light on exactly where these gains come from and gain insight into the sources of the most typical mistakes. Keyword: Computational Linguistics URL: https://doi.org/10.7717/peerj-cs.559 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237322/ BASE Hide details 20 Temporal Integration of Text Transcripts and Acoustic Features for Alzheimer's Diagnosis Based on Spontaneous Speech Martinc, Matej; Haider, Fasih; Pollak, Senja... In: Front Aging Neurosci (2021) BASE Show details Page: 1 2 3

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