Arantza Casillas

Also published as: A. Casillas


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Approximate Nearest Neighbour Extraction Techniques and Neural Networks for Suicide Risk Prediction in the CLPsych 2022 Shared Task
Hermenegildo Fabregat Marcos | Ander Cejudo | Juan Martinez-romo | Alicia Perez | Lourdes Araujo | Nuria Lebea | Maite Oronoz | Arantza Casillas
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

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.


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On the Contribution of Per-ICD Attention Mechanisms to Classify Health Records in Languages with Fewer Resources than English
Alberto Blanco | Sonja Remmer | Alicia Pérez | Hercules Dalianis | Arantza Casillas
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

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


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The impact of simple feature engineering in multilingual medical NER
Rebecka Weegar | Arantza Casillas | Arantza Diaz de Ilarraza | Maite Oronoz | Alicia Pérez | Koldo Gojenola
Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)

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.

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Fully unsupervised low-dimensional representation of adverse drug reaction events through distributional semantics
Alicia Pérez | Arantza Casillas | Koldo Gojenola
Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)

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.


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Adverse Drug Event prediction combining shallow analysis and machine learning
Sara Santiso | Arantza Casillas | Alicia Pérez | Maite Oronoz | Koldo Gojenola
Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)

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IxaMed: Applying Freeling and a Perceptron Sequential Tagger at the Shared Task on Analyzing Clinical Texts
Koldo Gojenola | Maite Oronoz | Alicia Pérez | Arantza Casillas
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)


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First Approaches on Spanish Medical Record Classification Using Diagnostic Term to Class Transduction
A. Casillas | A. Díaz de Ilarraza | K. Gojenola | M. Oronoz | Alicia Pérez
Proceedings of the 10th International Workshop on Finite State Methods and Natural Language Processing


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Using Kybots for Extracting Events in Biomedical Texts
Arantza Casillas | Arantza Díaz de Ilarraza | Koldo Gojenola | Maite Oronoz | German Rigau
Proceedings of BioNLP Shared Task 2011 Workshop

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Testing the Effect of Morphological Disambiguation in Dependency Parsing of Basque
Kepa Bengoetxea | Arantza Casillas | Koldo Gojenola
Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages


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Spanish-to-Basque MultiEngine Machine Translation for a Restricted Domain
Iñaki Alegria | Arantza Casillas | Arantza Diaz de Ilarraza | Jon Igartua | Gorka Labaka | Mikel Lersundi | Aingeru Mayor | Kepa Sarasola
Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers

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.


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Multilingual Document Clustering: An Heuristic Approach Based on Cognate Named Entities
Soto Montalvo | Raquel Martínez | Arantza Casillas | Víctor Fresno
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics


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DTD-driven bilingual document generation
Arantza Casillas | Joseba Abaitua | Raquel Martínez
INLG’2000 Proceedings of the First International Conference on Natural Language Generation

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Recycling annotated parallel corpora for bilingual document composition
Arantza Casillas | Joseba Abaitua | Raquel Martínez
Proceedings of the Fourth Conference of the Association for Machine Translation in the Americas: Technical Papers

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.


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Bitext Correspondences through Rich Mark-up
Raquel Martinez | Joseba Abaitua | Arantza Casillas
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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Bitext Correspondences through Rich Mark-up
Raquel Martinez | Joseba Abaitua | Arantza Casillas
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

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Aligning tagged bitexts
Raquel Martinez | Joseba Abaitua | Arantza Casillas
Sixth Workshop on Very Large Corpora