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Book Success Prediction with Pretrained Sentence Embeddings and Readability Scores
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| 2 |
Multi-National Topics Maps for Parliamentary Debate Analysis
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Supporting an effective review of telecollaboration for second language learning by visualising the participation and engagement at Dublin City University
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In: Lee, Hyowon orcid:0000-0003-4395-7702 , Scriney, Michael orcid:0000-0001-6813-2630 , Dey-Plissonneau, Aparajita and Smeaton, Alan orcid:0000-0003-1028-8389 (2021) Supporting an effective review of telecollaboration for second language learning by visualising the participation and engagement at Dublin City University. In: Virtual Exchange in Higher Education: Charting the Irish Experience, 17 Sept 2021, Online vs MS Teams. (2021)
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Contextualization of Web contents through semantic enrichment from linked open data ; Contextualisation des contenus Web par l'enrichissement sémantique à partir de données
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In: https://tel.archives-ouvertes.fr/tel-03561788 ; Databases [cs.DB]. Normandie Université, 2021. English. ⟨NNT : 2021NORMC243⟩ (2021)
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Usage of Visual Analytics to Support Immigration-Related, Personalised Language Training Scenarios ...
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Usage of Visual Analytics to Support Immigration-Related, Personalised Language Training Scenarios ...
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Toward a Better Understanding of Academic Programs Educational Objectives: A Data Analytics-Based Approach
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In: Applied Sciences ; Volume 11 ; Issue 20 (2021)
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LLOD-driven Bilingual Word Embeddings Rivaling Cross-lingual Transformers in Quality of Life Concept Detection from French Online Health Communities ...
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Abstract:
We describe the use of Linguistic Linked Open Data (LLOD) to support a cross-lingual transfer framework for concept detection in online health communities. Our goal is to develop multilingual text analytics as an enabler for analyzing health-related quality of life (HRQoL) from self-reported patient narratives. The framework capitalizes on supervised cross-lingual projection methods, so that labeled training data for a source language are sufficient and are not needed for target languages. Cross-lingual supervision is provided by LLOD lexical resources to learn bilingual word embeddings that are simultaneously tuned to represent an inventory of HRQoL concepts based on the World Health Organization’s quality of life surveys (WHOQOL). We demonstrate that lexicon induction from LLOD resources is a powerful method that yields rich and informative lexical resources for the cross-lingual concept detection task which can outperform existing domain-specific lexica. Furthermore, in a comparative evaluation we find ...
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Keyword:
Apertium RDF; Bilingual Word Embeddings; Cross-lingual Transformers; Health-related Quality of Life; Linguistic Linked Open Data; Multilingual Text Analytics
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URL: https://dx.doi.org/10.5281/zenodo.5011770
https://zenodo.org/record/5011770
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LLOD-driven Bilingual Word Embeddings Rivaling Cross-lingual Transformers in Quality of Life Concept Detection from French Online Health Communities ...
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LADDER. Learners' digital communication: a corpus for pragmatic competences in Italian L1/L2 ...
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LADDER. Learners' digital communication: a corpus for pragmatic competences in Italian L1/L2 ...
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LADDER. Learners' digital communication: a corpus for pragmatic competences in Italian L1/L2 ...
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English Machine Reading Comprehension Datasets: A Survey ; Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
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Neural Machine Translation for Conditional Generation of Novel Procedures
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Inferring the Relationship between Anxiety and Extraversion from Tweets during COVID19 – A Linguistic Analytics Approach
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Unsupervised Deep Learning for Fake Content Detection in Social Media
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Cognitive biases in developing biased Artificial Intelligence recruitment system
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Unpacking 'the Next Black Box': Investigating the Cognitive and Affective Underpinnings of Student Self-Assessment
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Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?
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In: Faculty Publications (2021)
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Artificial intelligence in educational assessment: ‘Breakthrough? Or buncombe and ballyhoo?’
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