Elena Alvarez-Mellado


2020

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An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines
Elena Alvarez-Mellado
Proceedings of the The 4th Workshop on Computational Approaches to Code Switching

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.

2019

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Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction
Elena Alvarez-Mellado | Eben Holderness | Nicholas Miller | Fyonn Dhang | Philip Cawkwell | Kirsten Bolton | James Pustejovsky | Mei-Hua Hall
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

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.