Suchergebnisse

The goal of the FASTPARSE project (Fast Natural Language Parsing for Large-Scale NLP), funded by the European Research Council (ERC), is to achieve a breakthrough in the speed of natural language syntactic parsers, developing fast parsers that are suitable for web-scale processing. For this purpose, the project proposes several research lines involving computational optimization, algorithmics, statistical analysis of language and cognitive models inspired in human language processing. ; El proyecto FASTPARSE (Fast Natural Language Parsing for Large-Scale NLP), financiado por el Consejo Europeo de Investigación (ERC), tiene como objetivo lograr un salto cualitativo en la velocidad de los analizadores sintácticos de lenguaje natural, desarrollando analizadores lo suficientemente rápidos para facilitar el procesado de textos a escala web. Para ello, el proyecto propone distintas líneas de investigación que combinan técnicas de optimización informática, algoritmia, análisis estadístico de propiedades del lenguaje y modelos cognitivos inspirados en el procesado humano del mismo. ; This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 714150).

RESUMENEl presente artículo examina el análisis efectuado por La Mothe le Vayer acerca del hecho religioso y su problemática en su obra Dialogues faits à l´imitation des anciens. Desde un acusado escepticismo, presenta la religión como una construcción cultural y convencional. La filosofía, en tanto ejercicio de análisis escéptico, ofrecerá una visión de la moral y la religión alejadas por completo de toda sacralizad. Serán, sin embargo, instrumentos muy útiles para el Estado y, si se administran bien, redundarán en la cohesión social y en la pax politica.PALABRAS CLAVEESCEPTICISMO, CONVENCIÓN, RAZÓN DE ESTADOABSTRACTIn this paper we examine the analysis carried out by La Mothe le Vayer of the religious matter and its problems in his work Dialogues faits à l´imitation des anciens. From a marked scepticism, he presents religion as a cultural and conventional entity. Philosophy, as an exercise of sceptical analysis, offers a view of morality and religion totally removed from any sacredness. Both are, however, very useful instruments for the State and, when well administered, beneficial to social cohesion and pax politica.KEY WORDSSCEPTICISM, CONVENTIOM, REASON OF STATE

En esta investigacion analizamos el caso de una empresa de Estados Unidos que decide introducir nuevas tecnicas de gestion. Un ambiguo proposito del presidente de la empresa y propietario de diferenciarse de un estilo de gestion paternalista del antiguo presidente y el papel fuertemente activador de la retorica impulsada por el guru de las ideas de Calidad Total W.Edwards Deming, contactos con consultores y otros directivos impulsan la implementacion del cambio. En el caso estudiado se mezclan tres discursos retoricos: El enfoque de calidad total impulsado por Deming, el enfoque "culturalista" basado en las ideas de Tom Peters, y la perspectiva de la autonomia "radical" de Ricardo Semler. El consenso de los miembros de la organización acerca del significado de la retorica que se utiliza se vuelve cada mas complejo y las concentradiciones y conflictos van en aumento generandose una creciente percepcion de ambigüedad por parte de los miembros de la empresa, esta ambigüedad es generada basicamente, a) por el significado abierto y equivoco del lenguaje que se utiliza y b) por los conflictos de significado producidos por la utilizacion de diferentes "paradigmas" de gestion. Finalmente el presidente de la empresa decide imponer unilateralmente limites a los significados del cambio abandonado la convocatoria a consensuar "democraticamente" el significado de las ideas de gestion impulsadas volviendose a una estructura jerarquizada cercana al tipo de organización que pretendia abandonarse

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG ; [Abstract] Dependency and constituent trees are widely used by many artificial intelligence applications for representing the syntactic structure of human languages. Typically, these structures are separately produced by either dependency or constituent parsers. In this article, we propose a transition-based approach that, by training a single model, can efficiently parse any input sentence with both constituent and dependency trees, supporting both continuous/projective and discontinuous/non-projective syntactic structures. To that end, we develop a Pointer Network architecture with two separate task-specific decoders and a common encoder, and follow a multitask learning strategy to jointly train them. The resulting quadratic system, not only becomes the first parser that can jointly produce both unrestricted constituent and dependency trees from a single model, but also proves that both syntactic formalisms can benefit from each other during training, achieving state-of-the-art accuracies in several widely-used benchmarks such as the continuous English and Chinese Penn Treebanks, as well as the discontinuous German NEGRA and TIGER datasets. ; We acknowledge the European Research Council (ERC), which has funded this research under the European Union's Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150), ERDF/MICINN-AEI (ANSWER-ASAP, TIN2017-85160-C2-1-R; SCANNER-UDC, PID2020-113230RB-C21), Xunta de Galicia, Spain (ED431C 2020/11), and Centro de Investigación de Galicia "CITIC", funded by Xunta de Galicia, Spain and the European Union (ERDF - Galicia 2014–2020 Program), by grant ED431G 2019/01. Funding for open access charge: Universidade da Coruña / CISUG ; Xunta de Galicia; ED431C 2020/11 ; Xunta de Galicia; ED431G 2019/01

[Abstract] Parsing is a core natural language processing technique that can be used to obtain the structure underlying sentences in human languages. Named entity recognition (NER) is the task of identifying the entities that appear in a text. NER is a challenging natural language processing task that is essential to extract knowledge from texts in multiple domains, ranging from financial to medical. It is intuitive that the structure of a text can be helpful to determine whether or not a certain portion of it is an entity and if so, to establish its concrete limits. However, parsing has been a relatively little-used technique in NER systems, since most of them have chosen to consider shallow approaches to deal with text. In this work, we study the characteristics of NER, a task that is far from being solved despite its long history; we analyze the latest advances in parsing that make its use advisable in NER settings; we review the different approaches to NER that make use of syntactic information; and we propose a new way of using parsing in NER based on casting parsing itself as a sequence labeling task. ; Xunta de Galicia; ED431C 2020/11 ; Xunta de Galicia; ED431G 2019/01 ; This work has been funded by MINECO, AEI and FEDER of UE through the ANSWER-ASAP project (TIN2017-85160-C2-1-R); and by Xunta de Galicia through a Competitive Reference Group grant (ED431C 2020/11). CITIC, as Research Center of the Galician University System, is funded by the Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF/FEDER) with 80%, the Galicia ERDF 2014-20 Operational Programme, and the remaining 20% from the Secretaría Xeral de Universidades (Ref. ED431G 2019/01). Carlos Gómez-Rodríguez has also received funding from the European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (FASTPARSE, Grant No. 714150).

[Abstract] Recent analyses suggest that encoders pretrained for language modeling capture certain morpho-syntactic structure. However, probing frameworks for word vectors still do not report results on standard setups such as constituent and dependency parsing. This paper addresses this problem and does full parsing (on English) relying only on pretraining architectures – and no decoding. We first cast constituent and dependency parsing as sequence tagging. We then use a single feed-forward layer to directly map word vectors to labels that encode a linearized tree. This is used to: (i) see how far we can reach on syntax modelling with just pretrained encoders, and (ii) shed some light about the syntax-sensitivity of different word vectors (by freezing the weights of the pretraining network during training). For evaluation, we use bracketing F1-score and LAS, and analyze in-depth differences across representations for span lengths and dependency displacements. The overall results surpass existing sequence tagging parsers on the PTB (93.5%) and end-to-end EN-EWT UD (78.8%). ; We thank Mark Anderson and Daniel Hershcovich for their comments. DV, MS and CGR are funded by the ERC under the European Union's Horizon 2020 research and innovation programme (FASTPARSE, grant No 714150), by the ANSWER-ASAP project (TIN2017-85160-C2-1-R) from MINECO, and by Xunta de Galicia (ED431B 2017/01). AS is funded by a Google Focused Research Award ; Xunta de Galicia; ED431B 2017/01

This paper presents a monolingual BERT model for Galician. We follow the recent trend that shows that it is feasible to build robust monolingual BERT models even for relatively low-resource languages, while performing better than the well-known official multilingual BERT (mBERT). More particularly, we release two monolingual Galician BERT models, built using 6 and 12 transformer layers, respectively; trained with limited resources (~45 million tokens on a single GPU of 24GB). We then provide an exhaustive evaluation on a number of tasks such as POS-tagging, dependency parsing and named entity recognition. For this purpose, all these tasks are cast in a pure sequence labeling setup in order to run BERT without the need to include any additional layers on top of it (we only use an output classification layer to map the contextualized representations into the predicted label). The experiments show that our models, especially the 12-layer one, outperform the results of mBERT in most tasks. ; Este artículo presenta un modelo BERT monolingüe para el gallego. Nos basamos en la tendencia actual que ha demostrado que es posible crear modelos BERT monolingües robustos incluso para aquellos idiomas para los que hay una relativa escasez de recursos, funcionando éstos mejor que el modelo BERT multilingüe oficial (mBERT). Concretamente, liberamos dos modelos monolingües para el gallego, creados con 6 y 12 capas de transformers, respectivamente, y entrenados con una limitada cantidad de recursos (~45 millones de palabras sobre una única GPU de 24GB.) Para evaluarlos realizamos un conjunto exhaustivo de experimentos en tareas como análisis morfosintáctico, análisis sintáctico de dependencias o reconocimiento de entidades. Para ello, abordamos estas tareas como etiquetado de secuencias, con el objetivo de ejecutar los modelos BERT sin la necesidad de incluir ninguna capa adicional (únicamente se añade la capa de salida encargada de transformar las representaciones contextualizadas en la etiqueta predicha). Los experimentos muestran que nuestros modelos, especialmente el de 12 capas, mejoran los resultados de mBERT en la mayor parte de las tareas. ; This work has received funding from the European Research Council (ERC), which has funded this research under the European Union's Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150), from MINECO (ANSWER-ASAP, TIN2017-85160-C2-1-R), from Xunta de Galicia (ED431C 2020/11), from Centro de Investigación de Galicia `CITIC', funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014-2020 Program), by grant ED431G 2019/01, and by Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), ERDF 2014-2020: Call ED431G 2019/04. DV is supported by a 2020 Leonardo Grant for Researchers and Cultural Creators from the BBVA Foundation. MG is supported by a Ramón y Cajal grant (RYC2019-028473-I).

[Abstract] In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements. ; Xunta de Galicia; ED431G 2019/01 ; Xunta de Galicia; ED431C 2020/11 ; This work has been funded by FEDER/Ministerio de Ciencia, Innovación y Universidades — Agencia Estatal de Investigación through the ANSWERASAP project (TIN2017-85160-C2-1-R); and by Xunta de Galicia through a Competitive Reference Group grant (ED431C 2020/11). CITIC, as Research Center of the Galician University System, is funded by the Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF/FEDER) with 80%, the Galicia ERDF 2014-20 Operational Programme, and the remaining 20% from the Secretaría Xeral de Universidades (ref. ED431G 2019/01). David Vilares is also supported by a 2020 Leonardo Grant for Researchers and Cultural Creators from the BBVA Foundation. Carlos Gómez-Rodríguez has also received funding from the European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (FASTPARSE, grant No. 714150)

A fundamental problem in network science is the normalization of the topological or physical distance between vertices, which requires understanding the range of variation of the unnormalized distances. Here we investigate the limits of the variation of the physical distance in linear arrangements of the vertices of trees. In particular, we investigate various problems of the sum of edge lengths in trees of a fixed size: the minimum and the maximum value of the sum for specific trees, the minimum and the maximum in classes of trees (bistar trees and caterpillar trees) and finally the minimum and the maximum for any tree. We establish some foundations for research on optimality scores for spatial networks in one dimension. ; RFC is supported by the grant TIN2017-89244-R from MINECO (Ministerio de Economía, Industria y Competitividad) and the recognition 2017SGR-856 (MACDA) from AGAUR (Generalitat de Catalunya). CGR is funded by the European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150), the ANSWER-ASAP project (TIN2017-85160-C2-1-R) from MINECO and Xunta de Galicia (ED431C 2020/11, ED431G2019/01, and an Oportunius program grant to complement ERC grants). JLE is funded by the grants TIN2016-76573-C2-1-P and PID2019-109137GB-C22 from MINECO. ; Peer Reviewed ; Postprint (author's final draft)