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LLOD-driven Bilingual Word Embeddings Rivaling Cross-lingual Transformers in Quality of Life Concept Detection from French Online Health Communities ...
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 ...
Keyword: Apertium RDF; Bilingual Word Embeddings; Cross-lingual Transformers; Health-related Quality of Life; Linguistic Linked Open Data; Multilingual Text Analytics
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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