On Left and Right Dislocation: A Dynamic Perspective

The paper argues that by modelling the incremental and left-right process of interpretation as a process of growth of logical form (representing logical forms as trees), an integrated typology of left-dislocation and right-dislocation phenomena becomes available, bringing out not merely the similarities between these types of phenomena, but also their asymmetry. The data covered include hanging topic left dislocation, clitic left dislocation, left dislocation, pronoun doubling, expletives, extraposition, and right node raising, with each set of data analysed in terms of general principles of tree growth. In the light of the success in providing a characterisation of the asymmetry between left and right periphery phenomena, a result not achieved in more wellknown formalisms, the paper concludes that grammar formalisms should model the dynamics of language processing in time.Articl

CLARA: A New Generation of Researchers in Common Language Resources and Their Applications

CLARA (Common Language Resources and Their Applications) is a Marie Curie Initial Training Network which ran from 2009 until 2014 with the aim of providing researcher training in crucial areas related to language resources and infrastructure. The scope of the project was broad and included infrastructure design, lexical semantic modeling, domain modeling, multimedia and multi-modal communication, applications, and parsing technologies and grammar models. An international consortium of 9 partners and 12 associate partners employed researchers in 19 new positions and organized a training program consisting of 10 thematic courses and summer/winter schools. The project has resulted in new theoretical insights as well as new resources and tools. Most importantly, the project has trained a new generation of researchers who can perform advanced research and development in language resources and technologies.Peer reviewe

Ensemble Parsing and its Effect on Machine Translation

The focus of much of dependency parsing is on creating new modeling techniques and examining new feature sets for existing dependency models. Often these new models are lucky to achieve equivalent results with the current state of the art results and often perform worse. These approaches are for languages that are often resource-rich and have ample training data available for dependency parsing. For this reason, the accuracy scores are often quite high. This, by its very nature, makes it quite difficult to create a significantly large increase in the current state-of-the-art. Research in this area is often concerned with small accuracy changes or very specific localized changes, such as increasing accuracy of a particular linguistic construction. With so many modeling techniques available to languages with large resources the problem exists on how to exploit the current techniques with the use of combination, or ensemble, techniques along with this plethora of data. Dependency parsers are almost ubiquitously evaluated on their accuracy scores, these scores say nothing of the complexity and usefulness of the resulting structures. The structures may have more complexity due to the depth of their co- ordination or noun phrases. As dependency parses are basic structures in which other systems are built upon, it would seem more reasonable to judge these parsers down the NLP pipeline. The types of parsing errors that cause significant problems in other NLP applications is currently an unknown

An Unsolicited Soliloquy on Dependency Parsing

Programa Oficial de Doutoramento en Computación . 5009V01[Abstract] This thesis presents work on dependency parsing covering two distinct lines of research. The first aims to develop efficient parsers so that they can be fast enough to parse large amounts of data while still maintaining decent accuracy. We investigate two techniques to achieve this. The first is a cognitively-inspired method and the second uses a model distillation method. The first technique proved to be utterly dismal, while the second was somewhat of a success. The second line of research presented in this thesis evaluates parsers. This is also done in two ways. We aim to evaluate what causes variation in parsing performance for different algorithms and also different treebanks. This evaluation is grounded in dependency displacements (the directed distance between a dependent and its head) and the subsequent distributions associated with algorithms and the distributions found in treebanks. This work sheds some light on the variation in performance for both different algorithms and different treebanks. And the second part of this area focuses on the utility of part-of-speech tags when used with parsing systems and questions the standard position of assuming that they might help but they certainly won’t hurt.[Resumen] Esta tesis presenta trabajo sobre análisis de dependencias que cubre dos líneas de investigación distintas. La primera tiene como objetivo desarrollar analizadores eficientes, de modo que sean suficientemente rápidos como para analizar grandes volúmenes de datos y, al mismo tiempo, sean suficientemente precisos. Investigamos dos métodos. El primero se basa en teorías cognitivas y el segundo usa una técnica de destilación. La primera técnica resultó un enorme fracaso, mientras que la segunda fue en cierto modo un ´éxito. La otra línea evalúa los analizadores sintácticos. Esto también se hace de dos maneras. Evaluamos la causa de la variación en el rendimiento de los analizadores para distintos algoritmos y corpus. Esta evaluación utiliza la diferencia entre las distribuciones del desplazamiento de arista (la distancia dirigida de las aristas) correspondientes a cada algoritmo y corpus. También evalúa la diferencia entre las distribuciones del desplazamiento de arista en los datos de entrenamiento y prueba. Este trabajo esclarece las variaciones en el rendimiento para algoritmos y corpus diferentes. La segunda parte de esta línea investiga la utilidad de las etiquetas gramaticales para los analizadores sintácticos.[Resumo] Esta tese presenta traballo sobre análise sintáctica, cubrindo dúas liñas de investigación. A primeira aspira a desenvolver analizadores eficientes, de maneira que sexan suficientemente rápidos para procesar grandes volumes de datos e á vez sexan precisos. Investigamos dous métodos. O primeiro baséase nunha teoría cognitiva, e o segundo usa unha técnica de destilación. O primeiro método foi un enorme fracaso, mentres que o segundo foi en certo modo un éxito. A outra liña avalúa os analizadores sintácticos. Esto tamén se fai de dúas maneiras. Avaliamos a causa da variación no rendemento dos analizadores para distintos algoritmos e corpus. Esta avaliaci´on usa a diferencia entre as distribucións do desprazamento de arista (a distancia dirixida das aristas) correspondentes aos algoritmos e aos corpus. Tamén avalía a diferencia entre as distribucións do desprazamento de arista nos datos de adestramento e proba. Este traballo esclarece as variacións no rendemento para algoritmos e corpus diferentes. A segunda parte desta liña investiga a utilidade das etiquetas gramaticais para os analizadores sintácticos.This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150) and from the Centro de Investigación de Galicia (CITIC) which is funded by the Xunta de Galicia and the European Union (ERDF - Galicia 2014-2020 Program) by grant ED431G 2019/01.Xunta de Galicia; ED431G 2019/0

Token-based typology and word order entropy: A study based on universal dependencies

The present paper discusses the benefits and challenges of token-based typology, which takes into account the frequencies of words and constructions in language use. This approach makes it possible to introduce new criteria for language classification, which would be difficult or impossible to achieve with the traditional, type-based approach. This point is illustrated by several quantitative studies of word order variation, which can be measured as entropy at different levels of granularity. I argue that this variation can be explained by general functional mechanisms and pressures, which manifest themselves in language use, such as optimization of processing (including avoidance of ambiguity) and grammaticalization of predictable units occurring in chunks. The case studies are based on multilingual corpora, which have been parsed using the Universal Dependencies annotation scheme