The application of Information Retrieval (IR) and deep learning strategies to explore the vast amount of rapidly growing health-related content is of utmost importance, but is also particularly challenging, due to the very specialized domain language, and implicit differences in language characteristics depending on the content type. This workshop aims at presenting and discussing current and future directions for IR and machine learning approaches devoted to the retrieval and classification of different types of health-related documents ranging from layman or patient generated texts to highly specialized medical literature or clinical records. It includes a session on the MESINESP shared task, supported by the Spanish National Language Technology plan (Plan TL), in order to address the importance and impact of community evaluation efforts, in particular BioASQ, BioCreative, eHealth CLEF, MEDIQA and TREC, as scenarios for exploring evaluation settings and generate data collections of key importance for promoting the development and comparison of IR resources. Additionally, an open session will address IR technologies for heterogeneous health-related content open to multiple languages with a particular interest in the exploitation of structured controlled vocabularies and entity linking, covering the following topics: multilingual and non-English health-related IR, concept indexing, text categorization, generation of evaluation resources biomedical document IR strategies; scalability, robustness and reproducibility of health IR and text mining resources; use of specialized machine translation and advanced deep learning approaches for improving health related search results; medical Question Answering search tools; retrieval of multilingual health related web-content; and other related topics.
Proposal of the First International Workshop on Semantic Indexing and Information Retrieval for Health from Heterogeneous Content Types and Languages (SIIRH)