A Machine Learning based Central Unit Detector for Basque Scientific Texts

En este artículo presentamos el primer detector de la Unidad Central (UC) de resúmenes científicos en euskera basado en técnicas de aprendizaje automático. Después de segmentar el texto en unidades de discurso elementales, la detección de la unidad central es crucial para anotar de forma más fiable la estructura relacional de textos bajo la Teoría de la Estructura Retórica o Rhetorical Structure Theory (RST). Además, la unidad central puede ser explotada en diversas tareas como resumen automático, tareas de pregunta y respuesta o análisis del sentimiento. Los resultados obtenidos demuestran que las técnicas de aprendizaje automático superan a las técnicas basadas en reglas a pesar del pequeño tamaño del corpus y de la heterogeneidad de los dominios que éste muestra, dejando todavía lugar para mejoras y desarrollo.This paper presents an automatic detector of the discourse central unit (CU) in scientific abstracts based on machine learning techniques. After segmenting a text in its elementary discourse units, the detection of the central unit is a crucial step on the way to robustly build discourse trees under the Rhetorical Structure Theory (RST). Besides, CU detection may also be useful in automatic summarization, question answering and sentiment analysis tasks. Results show that the CU detection using machine learning techniques for Basque scientific abstracts outperform rule based techniques, even on a small size corpus on different domains. This leads us to think that there is still room for improvement.Este trabajo ha sido financiado en parte por el siguiente proyecto: TIN2015-65308-C5-1-R (MINECO/FEDER)

Advances in monolingual and crosslingual automatic disability annotation in Spanish

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)

Resumen de la tarea de ClinAIS en IberLEF 2023: Identificación Automática de Secciones en Documentos Clínicos en Castellano

The ClinAIS shared task organized by IOMED and the HiTZ center aims to tackle the identification of seven section types within unstructured clinical records in the Spanish language. These records, known as Electronic Clinical Narratives (ECNs), store crucial individual health information. However, their lack of standardized formats poses challenges in the development and evaluation of automated systems for clinical document analysis. Twenty-seven participants registered for the task, with five submitting results. This paper presents the outcomes and methodologies used in ClinAIS, contributing to the advancement of clinical text analysis and its application in improving healthcare decision-making and patient care.La tarea ClinAIS organizada por IOMED y el centro HiTZ tiene como objetivo abordar la identificación de siete tipos de secciones dentro de registros clínicos no-estructurados en español. Estos registros, conocidos como Narrativas Clínicas Electrónicas (ECNs), almacenan información crucial acerca de la salud personal. Sin embargo, la falta de estandarización en los formatos plantea desafíos en el desarrollo y evaluación de sistemas automatizados para el análisis de documentos clínicos. Veintisiete participantes se registraron para la tarea, de los cuales cinco presentaron resultados. Este artículo presenta los resultados y metodologías utilizadas en la tarea ClinAIS, contribuyendo al avance del análisis de notas clínicas y su aplicación en la mejora de la toma de decisiones en la atención médica y el cuidado al paciente.This work was partially funded by the Spanish Ministry of Science and Innovation (MCI/AEI/FEDER, UE, DOTTHEALTH/PAT-MED PID2019-106942RB-C31), the Basque Government (IXA IT1570-22), MCIN/AEI/ 10.13039/501100011033, European Union NextGeneration EU/PRTR (DeepR3 TED2021-130295B-C31, ANTIDOTE PCI2020-120717-2 EU ERA-Net CHIST-ERA), and the Government of the United States IARPA BETTER program (INT NOCORE 19/08 project, via Contract No. 2019-19051600006)

Construcción de un corpus etiquetado sintácticamente para el euskera

El objetivo de este trabajo es la construcción de un corpus anotado sintácticamente para el euskera. En esta comunicación presentaremos, en primer lugar, las bases sobre las que se asienta nuestro etiquetado. Tras examinar diversas opciones se optó por el esquema presentado por (Carrol et al., 1998). Este esquema sigue los estándares EAGLES y se basa en la idea de añadir a cada frase del corpus una serie de relaciones gramaticales que especifican la dependencia existente entre el núcleo y sus modificadores. Una vez presentado el formalismo de etiquetado, se expondrán los problemas que hemos encontrado en nuestra tarea y las decisiones tomadas. Seguidamente se describirá un ejemplo concreto en el que se muestra la aplicación de dicho esquema sobre un corpus inicial. Finalmente, presentaremos las conclusiones sobre la idoneidad del esquema al euskera y trabajo futuro.The aim of this work is the construction of a syntactically annotated treebank for Basque. In this paper we present first, the basis of the annotation. After examining several options we chose the scheme presented in (Carrol et al., 1998). It follows the EAGLES standards and it is based on the idea of adding to each sentence in the corpus a series of grammatical relations specifying the dependencies between modifiers and their nucleus. After the formalism has been presented, we will describe the problems we have found and the decisions we have taken to solve them. Next we present an example showing the application of the scheme to an initial corpus. Finally, we present the main conclusions about the applicability to Basque and future work.Este trabajo se ha realizado dentro del proyecto "Construcción de una base de datos de árboles sintácticos y semánticos", subvencionado por el Ministerio de Educación y Ciencia (PROFIT: FIT-150500-2002-244)

Extracción masiva de información sobre subcategorización verbal vasca a partir de corpus

En este artículo presentamos el trabajo realizado en la extracción automática de información sobre la aparición de complementos y adjuntos para un conjunto de 1.400 verbos a partir de un corpus periodístico de un millón y medio de palabras. Los resultados han sido evaluados, obteniéndose una precisión y cobertura satisfactorias. Estos datos se usarán para la adquisición manual y automática de información sobre subcategorización verbal

Towards a top-down approach for an automatic discourse analysis for Basque: Segmentation and Central Unit detection tool

Lately, discourse structure has received considerable attention due to the benefits its application offers in several NLP tasks such as opinion mining, summarization, question answering, text simplification, among others. When automatically analyzing texts, discourse parsers typically perform two different tasks: i) identification of basic discourse units (text segmentation) ii) linking discourse units by means of discourse relations, building structures such as trees or graphs. The resulting discourse structures are, in general terms, accurate at intra-sentence discourse-level relations, however they fail to capture the correct inter-sentence relations. Detecting the main discourse unit (the Central Unit) is helpful for discourse analyzers (and also for manual annotation) in improving their results in rhetorical labeling. Bearing this in mind, we set out to build the first two steps of a discourse parser following a top-down strategy: i) to find discourse units, ii) to detect the Central Unit. The final step, i.e. assigning rhetorical relations, remains to be worked on in the immediate future. In accordance with this strategy, our paper presents a tool consisting of a discourse segmenter and an automatic Central Unit detector.This study was carried out within the framework of the following projects: IXA Group: natural language processing IT1343-19 (Basque Government), DL4NLP KK-2019/00045 (Basque Government), PROSA-MED TIN2016-77820-C3-1-R (MINECO) and DeepReading: RTI2018-096846-B-C21 (MCIU/AEI/FEDER, UE)

Exaeskalarako sare-interkonexioen diseinurako helburu-aniztasuneko optimizazioa

Exascale performance will be delivered by systems composed of millions of inter-connected computing cores. The way these computing elements are connected with each other (network topology) has a strong impact on many performance characteristics. In this work we propose a multi-objective optimization- based framework to explore possible network topologies to be implemented in the EU-funded ExaNeSt project. The modular design of this system’s inter-connect provides great flexibility to design topologies optimized for specific performance targets such as communications locality, fault tolerance or energy-consumption. The generation proce-dure of the topologies is formulated as a three-objective optimization problem (minimizing some topological characteristics) where solutions are searched using evolutionary techniques. The analysis of the results, carried out using simulation, shows that the topologies meet the required performance objectives. In addition, a comparison with a well-known topology reveals that the generated solutions can provide better topological characteristics and also higher throughput in almost all evaluated scenarios.; Exaeskala errendimendua milioika kalkulu-nukleoz osaturiko sistemak erabi-liz lortuko da. Elementu horiek konektatzeko moduak (sare-topologia) izugarrizko eragina du hainbat errendimendu ezaugarritan. Lan honetan, sare-topologiak diseinatzeko helburu-anizta-suneko optimizazioan oinarritutako ingurune bat proposatzen dugu, EBk finantzatuta ExaNeSt proiektuan garatzen ari garena. ExaNeSt sistemaren sarearen modulartasunari esker sare-topolo-gia ezberdinak diseinatu ditzakegu hainbat errendimendu-helburu optimizatzeko; esaterako, in-guruko komunikazioak, hutsegite-tolerantzia eta energia-kontsumoa. Topologiak sortzeko pro-zesua optimizazio bidez gauzatzen da (sare-topologiaren hainbat ezaugarri minimizatuz) teknika ebolutiboak erabilita. Simulazio bidezko emaitzen analisiak sortutako topologiek errendimen-du-helburuak betetzen dituztela erakusten du. Gainera, sare-topologia ezagun batekin egindako konparazioan ikus daiteke gure proposamenak sortzen dituen sareek propietate topologiko ho-beak dauzkatela eta, aldi berean, errendimendu handiagoa lortzen dutela

Dependentzia Unibertsalen eredura egokitutako euskarazko zuhaitz-bankua

Hizkuntzaren Prozesamenduan kokatzen den Dependentzia Uniber-tsalen proiektuaren helburua da hainbat hizkuntzatan sortu diren dependentzia-ereduan oinarritutako zuhaitz-bankuak etiketatze-eskema estandar berera egokitzea. Artikulu honetan, eredu horretara automatikoki egokitu den euskarazko zuhaitz-bankua aurkez-ten da; halaber, egokitzapen-lan hori nola gauzatu den deskribatzen da eta, azkenik, ho-rretan oinarrituta, azaltzen da zer antzekotasun eta zer desberdintasun diren jatorrizko zuhaitza-bankuaren eta Dependentzia Unibertsalen eredura egokitutako zuhaitz-ban-kuaren artean.; In the Natural Language Processing research area, the aim of the Uni-versal Dependencies project is to convert dependency based treebanks developed in different languages into the same standard tagging scheme. This article presents the automatic conversion of the previously existing Basque treebank into this universal tagging scheme. This work describes how the conversion process has been carried out and highlights the similarities and differences between the original Basque treebank and the Universal Dependency based version of it