Negation and speculation processing: A study on cue-scope labelling and assertion classification in Spanish clinical text.

Natural Language Processing (NLP) based on new deep learning technology is contributing to the emergence of powerful solutions that help healthcare providers and researchers discover valuable patterns within insurmountable volumes of health records and scientific literature. Fundamental to the success of such solutions is the processing of negation and speculation. The article addresses this problem with state-of-the-art deep learning approaches from two perspectives: cue and scope labelling, and assertion classification. In light of the real struggle to access clinical annotated data, the study (a) proposes a methodology to automatically convert cue-scope annotations to assertion annotations; and (b) includes a range of scenarios with varying amounts of training data and adversarial test examples. The results expose the clear advantage of Transformer-based models in this regard, managing to overpass a series of baselines and the related work in the public corpus NUBes of clinical Spanish text.

Cross-lingual semantic annotation of biomedical literature: experiments in Spanish and English.

MOTIVATION: Biomedical literature is one of the most relevant sources of information for knowledge mining in the field of Bioinformatics. In spite of English being the most widely addressed language in the field; in recent years, there has been a growing interest from the natural language processing community in dealing with languages other than English. However, the availability of language resources and tools for appropriate treatment of non-English texts is lacking behind. Our research is concerned with the semantic annotation of biomedical texts in the Spanish language, which can be considered an under-resourced language where biomedical text processing is concerned. RESULTS: We have carried out experiments to assess the effectiveness of several methods for the automatic annotation of biomedical texts in Spanish. One approach is based on the linguistic analysis of Spanish texts and their annotation using an information retrieval and concept disambiguation approach. A second method takes advantage of a Spanish-English machine translation process to annotate English documents and transfer annotations back to Spanish. A third method takes advantage of the combination of both procedures. Our evaluation shows that a combined system has competitive advantages over the two individual procedures. AVAILABILITY AND IMPLEMENTATION: UMLSMapper (https://snlt.vicomtech.org/umlsmapper) and the annotation transfer tool (http://scientmin.taln.upf.edu/anntransfer/) are freely available for research purposes as web services and/or demos. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.