Increasingly, multilingual language resources are available as Linguistic Linked Open Data (LLOD)  which model relations between resources and include rich metadata with standardized, non-proprietary technologies – a trend which promises to lead to improved multilingual NLP systems. However, how to effectively utilize these resources in practical applications is not self-evident, in particular for specialized technical domains.
One example of such a domain are posts from online health communities, i.e., web fora and similar systems focusing on health topics used by patients, caregivers and/or professionals in a wide range of languages. Online health communities are a relevant data source for a range of emerging application areas, such as public health monitoring or evidence generation for regulatory drug approval , which entail analysing patients’ experiences beyond clinical trials. A central aspect of these so-called patient-reported outcomes is health-related quality of life (HRQoL) .
In a recent publication to be presented at the upcoming SEMANTiCS 2021 conference, Prêt-à-LLOD pilot partner Semalytix reports on a machine learning approach to classify online health community posts into categories derived from facets of HRQoL as described in the World Health Organization’s quality of life surveys (WHOQOL) , e.g., pain and discomfort, work capacity, financial resources. Semalytix addresses the problem of predicting HRQoL facets across languages via a multitude of individual binary classifiers trained using a cross-lingual transfer learning framework based on bilingual lexica available as multilingual LLOD. The combination of LLOD and transfer learning is motivated by the flexibility required to predict a large number of HRQoL facets (a total of 19 facets is considered) in a multilingual setting: Transfer learning allows to train classifiers for different languages based on training data in a single source language, without the need of additional annotated data for each target language.
The Semalytix approach is based on word embeddings and crosslingual supervision via token-level lexica (supervised bilingual word embeddings). Thus, the training procedure and resulting models are considerably less complex than state-of-the-art cross-lingual zero-shot models, which are based on contextualized representations learnt via pre-training transformer-based language models on massive multilingual corpora. Evaluation results presented show that the models developed by Semalytix, when being combined with a baseline approach that integrates machine translation and rule-based extraction algorithms, are strong contestants to cross-lingual transformers, thus emphasizing the prospects of resources and technologies being developed in Prêt-à-LLOD for rea-world multilingual text analytics applications.
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