Background
Unlike diseases, automatic recognition of disabilities has not received the same attention in the area of medical NLP. Progress in this direction is hampered by obstacles like the lack of annotated corpus. Neural architectures learn to translate sequences from spontaneous representations into their corresponding standard representations given a set of samples. The aim of this paper is to present the last advances in monolingual (Spanish) and crosslingual (from English to Spanish and vice versa) automatic disability annotation. The task consists of identifying disability mentions in medical texts written in Spanish within a collection of abstracts from journal papers related to the biomedical domain.
Results
In order to carry out the task, we have combined deep learning models that use different embedding granularities for sequence to sequence tagging with a simple acronym and abbreviation detection module to boost the coverage.
Conclusions
Our monolingual experiments demonstrate that a good combination of different word embedding representations provide better results than single representations, significantly outperforming the state of the art in disability annotation in Spanish. Additionally, we have experimented crosslingual transfer (zero-shot) for disability annotation between English and Spanish with interesting results that might help overcoming the data scarcity bottleneck, specially significant for the disabilities.This work was partially funded by the Spanish Ministry of Science and Innovation (MCI/AEI/FEDER, UE, DOTT-HEALTH/PAT-MED PID2019-106942RB-C31), the Basque Government (IXA IT1570-22), MCIN/AEI/ 10.13039/501100011033 and European Union NextGeneration EU/PRTR (DeepR3, TED2021-130295B-C31) and the EU ERA-Net CHIST-ERA and the Spanish Research Agency (ANTIDOTE PCI2020-120717-2)
