The extraction of anglicisms (lexical borrowings from English) is relevant both for lexicographic purposes and for NLP downstream tasks. We introduce a corpus of European Spanish newspaper headlines annotated with anglicisms and a baseline model for anglicism extraction. In this paper we present: (1) a corpus of 21,570 newspaper headlines written in European Spanish annotated with emergent anglicisms and (2) a conditional random field baseline model with handcrafted features for anglicism extraction. We present the newspaper headlines corpus, describe the annotation tagset and guidelines and introduce a CRF model that can serve as baseline for the task of detecting anglicisms. The presented work is a first step towards the creation of an anglicism extractor for Spanish newswire.

This paper documents a corpus of political speeches in Spanish. The documents in the corpus belong to the Christmas speeches that have been delivered yearly by the head of state of Spain since 1937. The historical period covered by these speeches ranges from the Spanish Civil War and the Francoist dictatorship up until today. As a result, the corpus reflects some of the most significant events and political changes in the recent history of Spain. Up until now, the speeches as a whole had not been collected into a single, systematic and reusable resource, as most of the texts were scattered among different sources. The paper describes: (1) the composition of the corpus; (2) the Python interface that facilitates querying and analyzing the corpus using the NLTK and spaCy libraries and (3) a set of HTML visualizations aimed at the general public to navigate the corpus and explore differences between TF-IDF frequencies.

Predicting which patients are more likely to be readmitted to a hospital within 30 days after discharge is a valuable piece of information in clinical decision-making. Building a successful readmission risk classifier based on the content of Electronic Health Records (EHRs) has proved, however, to be a challenging task. Previously explored features include mainly structured information, such as sociodemographic data, comorbidity codes and physiological variables. In this paper we assess incorporating additional clinically interpretable NLP-based features such as topic extraction and clinical sentiment analysis to predict early readmission risk in psychiatry patients.