ASKHi: Analisi sintaktiko konputazional hibridoa paradigma desberdinen konbinazioan oinarrituta

206 p.Hizkuntzaren Prozesamenduan sintaxiak berebiziko garrantzia du. Hainbat atazatan erabiltzen da sintaxitik eratorritako informazioa, esaterako itzulpen automatikoan, rol semantikoen etiketatzean eta sentimenduen analisian. Tesi-lan honetan sintaxi konputazionala landu da, zehazki dependentzietanoinarritutako sintaxia jorratu da analizatzaile automatikoen bidez. Dependentzien analisi sintaktiko automatikoa hobetzeko bide desberdinak aztertudira: izaera desberdinetako analizatzaileen hibridazioa, ezaugarrien ingeniaritzako tekniken erabilpena, multzokatze mota desberdinen esperimentazioaeta automatikoki analizatutako zuhaitz-bankuetatik eratorritako ezaugarrien erabilpena.Bide horiek guztiak jorratzearen arrazoi nagusia morfologikoki aberatsak diren hizkuntzen dependentzien analisia hobetzen lagundu dezaketen era des-berdinak aztertzea da. Hori dela eta, egindako esperimentu gehienak bost hizkuntza desberdinetan probatu dira (euskara, frantsesa, alemana, hunga-riera eta suediera), eta hizkuntza horietan guztietan probatu ezin izan diren bideak euskararekin probatu dira, euskararen dependentzien analisia baitabereziki hobetu nahi dena.Tesi-lan honen beste atal garrantzitsua euskararako baliabideak sortzea da, sintaxiari hertsiki lotutako baliabideak hain zuzen ere. Tesiak iraun di-tuen urteetan baliabide desberdinak sortu dira, baina bi dira nabarmentzeko modukoak. Alde batetik, 150 milioi hitzeko zuhaitz-bankua etiketatu dasintaktikoki era automatikoan; beste aldetik, euskarazko jatorrizko zuhaitz-bankua nazioarteko Dependentzia Unibertsalak proiektuan proposatzen denformatura bihurtu da. Lehenengo corpusa tesi-lan honetan erabili da automatikoki analizatutako zuhaitz-bankuetatik eratorritako ezaugarriak sortzeko,eta bigarrena edozeinek erabil dezake, publikoki atzigarri baitago

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)

HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine

Providing high quality explanations for AI predictions based on machine learning is a challenging and complex task. To work well it requires, among other factors: selecting a proper level of generality/specificity of the explanation; considering assumptions about the familiarity of the explanation beneficiary with the AI task under consideration; referring to specific elements that have contributed to the decision; making use of additional knowledge (e.g. expert evidence) which might not be part of the prediction process; and providing evidence supporting negative hypothesis. Finally, the system needs to formulate the explanation in a clearly interpretable, and possibly convincing, way. Given these considerations, ANTIDOTE fosters an integrated vision of explainable AI, where low-level characteristics of the deep learning process are combined with higher level schemes proper of the human argumentation capacity. ANTIDOTE will exploit cross-disciplinary competences in deep learning and argumentation to support a broader and innovative view of explainable AI, where the need for high-quality explanations for clinical cases deliberation is critical. As a first result of the project, we publish the Antidote CasiMedicos dataset to facilitate research on explainable AI in general, and argumentation in the medical domain in particular.Comment: To appear: In SEPLN 2023: 39th International Conference of the Spanish Society for Natural Language Processin

Overview of the SPMRL 2013 shared task: cross-framework evaluation of parsing morphologically rich languages

This paper reports on the first shared task on statistical parsing of morphologically rich languages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the evaluation metrics for parsing MRLs given different representation types. We present and analyze parsing results obtained by the task participants, and then provide an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios

Overview of the SPMRL 2013 Shared Task: A Cross-Framework Evaluation of Parsing Morphologically Rich Languages

International audienceThis paper reports on the first shared task on statistical parsing of morphologically rich lan- guages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the eval- uation metrics for parsing MRLs given dif- ferent representation types. We present and analyze parsing results obtained by the task participants, and then provide an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios

Deep dive machine translation

Machine Translation (MT) is one of the oldest language technologies having been researched for more than 70 years. However, it is only during the last decade that it has been widely accepted by the general public, to the point where in many cases it has become an indispensable tool for the global community, supporting communication between nations and lowering language barriers. Still, there remain major gaps in the technology that need addressing before it can be successfully applied in under-resourced settings, can understand context and use world knowledge. This chapter provides an overview of the current state-of-the-art in the field of MT, offers technical and scientific forecasting for 2030, and provides recommendations for the advancement of MT as a critical technology if the goal of digital language equality in Europe is to be achieved

Relatório de estágio em farmácia comunitária

Relatório de estágio realizado no âmbito do Mestrado Integrado em Ciências Farmacêuticas, apresentado à Faculdade de Farmácia da Universidade de Coimbr

ASKHi: Analisi sintaktiko konputazional hibridoa paradigma desberdinen konbinazioan oinarrituta

206 p.Hizkuntzaren Prozesamenduan sintaxiak berebiziko garrantzia du. Hainbat atazatan erabiltzen da sintaxitik eratorritako informazioa, esaterako itzulpen automatikoan, rol semantikoen etiketatzean eta sentimenduen analisian. Tesi-lan honetan sintaxi konputazionala landu da, zehazki dependentzietanoinarritutako sintaxia jorratu da analizatzaile automatikoen bidez. Dependentzien analisi sintaktiko automatikoa hobetzeko bide desberdinak aztertudira: izaera desberdinetako analizatzaileen hibridazioa, ezaugarrien ingeniaritzako tekniken erabilpena, multzokatze mota desberdinen esperimentazioaeta automatikoki analizatutako zuhaitz-bankuetatik eratorritako ezaugarrien erabilpena.Bide horiek guztiak jorratzearen arrazoi nagusia morfologikoki aberatsak diren hizkuntzen dependentzien analisia hobetzen lagundu dezaketen era des-berdinak aztertzea da. Hori dela eta, egindako esperimentu gehienak bost hizkuntza desberdinetan probatu dira (euskara, frantsesa, alemana, hunga-riera eta suediera), eta hizkuntza horietan guztietan probatu ezin izan diren bideak euskararekin probatu dira, euskararen dependentzien analisia baitabereziki hobetu nahi dena.Tesi-lan honen beste atal garrantzitsua euskararako baliabideak sortzea da, sintaxiari hertsiki lotutako baliabideak hain zuzen ere. Tesiak iraun di-tuen urteetan baliabide desberdinak sortu dira, baina bi dira nabarmentzeko modukoak. Alde batetik, 150 milioi hitzeko zuhaitz-bankua etiketatu dasintaktikoki era automatikoan; beste aldetik, euskarazko jatorrizko zuhaitz-bankua nazioarteko Dependentzia Unibertsalak proiektuan proposatzen denformatura bihurtu da. Lehenengo corpusa tesi-lan honetan erabili da automatikoki analizatutako zuhaitz-bankuetatik eratorritako ezaugarriak sortzeko,eta bigarrena edozeinek erabil dezake, publikoki atzigarri baitago

Reusability of the Basque Dependency Treebank for building the Gold Standard of Constraint Grammar Surface Syntax

El objetivo del trabajo consiste en reutilizar el Treebank de dependencias EPECDEP (BDT) para construir el gold standard de la sintaxis superficial del euskera. El paso básico consiste en el estudio comparativo de los dos formalismos aplicados sobre el mismo corpus: el formalismo de la Gramática de Restricciones (Constraint Grammar, CG) y la Gramática de Dependencias (Dependency Grammar, DP). Como resultado de dicho estudio hemos establecido los criterios lingüísticos necesarios para derivar la funciones sintácticas en estilo CG. Dichos criterios han sido implementados y evaluados, así en el 75% de los casos se derivan automáticamente las funciones sintácticas para construir el gold standard.The aim of the work is to profit the existing dependency Treebank EPEC-DEP (BDT) in order to build the gold standard for the surface syntax of Basque. As basic step, we make a comparative study of both formalisms, the Constraint Grammar formalism (CG) and the Dependency Grammar (DP) that have been applied on the corpus. As a result, we establish some criteria that will serve us to derive automatically the CG style syntactic function tags. Those criteria were implemented and evaluated; as a result, in the 75 % of the cases we are able to derive the CG style syntactic function tags for building the gold standard.Este trabajo ha sido financiado por el Gobierno Vasco (IT344-10)