This paper describes the participation of our group on the CLPsych 2022 shared task.For task A, which tries to capture changes in mood over time, we have applied an Approximate Nearest Neighbour (ANN) extraction technique with the aim of relabelling the user messages according to their proximity, based on the representation of these messages in a vector space. Regarding the subtask B, we have used the output of the subtask A to train a Recurrent Neural Network (RNN) to predict the risk of suicide at the user level.The results obtained are very competitive considering that our team was one of the few that made use of the organisers’ proposed virtual environment and also made use of the Task A output to predict the Task B results.

We introduce a multi-label text classifier with per-label attention for the classification of Electronic Health Records according to the International Classification of Diseases. We apply the model on two Electronic Health Records datasets with Discharge Summaries in two languages with fewer resources than English, Spanish and Swedish. Our model leverages the BERT Multilingual model (specifically the Wikipedia, as the model have been trained with 104 languages, including Spanish and Swedish, with the largest Wikipedia dumps) to share the language modelling capabilities across the languages. With the per-label attention, the model can compute the relevance of each word from the EHR towards the prediction of each label. For the experimental framework, we apply 157 labels from Chapter XI – Diseases of the Digestive System of the ICD, which makes the attention especially important as the model has to discriminate between similar diseases. 1 https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages

The goal of this paper is to examine the impact of simple feature engineering mechanisms before applying more sophisticated techniques to the task of medical NER. Sometimes papers using scientifically sound techniques present raw baselines that could be improved adding simple and cheap features. This work focuses on entity recognition for the clinical domain for three languages: English, Swedish and Spanish. The task is tackled using simple features, starting from the window size, capitalization, prefixes, and moving to POS and semantic tags. This work demonstrates that a simple initial step of feature engineering can improve the baseline results significantly. Hence, the contributions of this paper are: first, a short list of guidelines well supported with experimental results on three languages and, second, a detailed description of the relevance of these features for medical NER.

Electronic health records show great variability since the same concept is often expressed with different terms, either scientific latin forms, common or lay variants and even vernacular naming. Deep learning enables distributional representation of terms in a vector-space, and therefore, related terms tend to be close in the vector space. Accordingly, embedding words through these vectors opens the way towards accounting for semantic relatedness through classical algebraic operations. In this work we propose a simple though efficient unsupervised characterization of Adverse Drug Reactions (ADRs). This approach exploits the embedding representation of the terms involved in candidate ADR events, that is, drug-disease entity pairs. In brief, the ADRs are represented as vectors that link the drug with the disease in their context through a recursive additive model. We discovered that a low-dimensional representation that makes use of the modulus and argument of the embedded representation of the ADR event shows correlation with the manually annotated class. Thus, it can be derived that this characterization results in to be beneficial for further classification tasks as predictive features.

We present our initial strategy for Spanish-to-Basque MultiEngine Machine Translation, a language pair with very different structure and word order and with no huge parallel corpus available. This hybrid proposal is based on the combination of three different MT paradigms: Example-Based MT, Statistical MT and Rule- Based MT. We have evaluated the system, reporting automatic evaluation metrics for a corpus in a test domain. The first results obtained are encouraging.

Parallel corpora enriched with descriptive annotations facilitate multilingual authoring development. Departing from an annotated bitext we show how SGML markup can be recycled to produce complementary language resources. On the one hand, several translation memory databases together with glossaries of proper nouns have been produced. On the other, DTDs for source and target documents have been derived and put into correspondence. This paper discusses how these resources have been automatically generated and applied to an interactive bilingual authoring system. This tool is capable of handling a substantial proportion of text both in the composition and translation of structured documents.