3,099 research outputs found
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
Viability of Sequence Labeling Encodings for Dependency Parsing
Programa Oficial de Doutoramento en Computación . 5009V01[Abstract]
This thesis presents new methods for recasting dependency parsing as
a sequence labeling task yielding a viable alternative to the traditional
transition- and graph-based approaches. It is shown that sequence labeling
parsers provide several advantages for dependency parsing, such
as: (i) a good trade-off between accuracy and parsing speed, (ii) genericity
which enables running a parser in generic sequence labeling software
and (iii) pluggability which allows using full parse trees as features to
downstream tasks.
The backbone of dependency parsing as sequence labeling are the encodings
which serve as linearization methods for mapping dependency
trees into discrete labels, such that each token in a sentence is associated
with a label. We introduce three encoding families comprising: (i)
head selection, (ii) bracketing-based and (iii) transition-based encodings
which are differentiated by the way they represent a dependency
tree as a sequence of labels. We empirically examine the viability of
the encodings and provide an analysis of their facets.
Furthermore, we explore the feasibility of leveraging external complementary
data in order to enhance parsing performance. Our sequence
labeling parser is endowed with two kinds of representations. First,
we exploit the complementary nature of dependency and constituency
parsing paradigms and enrich the parser with representations from both
syntactic abstractions. Secondly, we use human language processing
data to guide our parser with representations from eye movements.
Overall, the results show that recasting dependency parsing as sequence
labeling is a viable approach that is fast and accurate and provides
a practical alternative for integrating syntax in NLP tasks.[Resumen]
Esta tesis presenta nuevos métodos para reformular el análisis sintáctico
de dependencias como una tarea de etiquetado secuencial, lo
que supone una alternativa viable a los enfoques tradicionales basados
en transiciones y grafos. Se demuestra que los analizadores de etiquetado
secuencial ofrecen varias ventajas para el análisis sintáctico de
dependencias, como por ejemplo (i) un buen equilibrio entre la precisión
y la velocidad de análisis, (ii) la genericidad que permite ejecutar
un analizador en un software genérico de etiquetado secuencial y (iii)
la conectividad que permite utilizar el árbol de análisis completo como
características para las tareas posteriores.
El pilar del análisis sintáctico de dependencias como etiquetado secuencial
son las codificaciones que sirven como métodos de linealización
para transformar los árboles de dependencias en etiquetas discretas, de
forma que cada token de una frase se asocia con una etiqueta. Introducimos
tres familias de codificación que comprenden: (i) selección de
núcleos, (ii) codificaciones basadas en corchetes y (iii) codificaciones basadas
en transiciones que se diferencian por la forma en que representan
un árbol de dependencias como una secuencia de etiquetas. Examinamos
empíricamente la viabilidad de las codificaciones y ofrecemos un
análisis de sus facetas.
Además, exploramos la viabilidad de aprovechar datos complementarios
externos para mejorar el rendimiento del análisis sintáctico. Dotamos
a nuestro analizador sintáctico de dos tipos de representaciones. En
primer lugar, explotamos la naturaleza complementaria de los paradigmas
de análisis sintáctico de dependencias y constituyentes, enriqueciendo
el analizador sintáctico con representaciones de ambas abstracciones
sintácticas. En segundo lugar, utilizamos datos de procesamiento del
lenguaje humano para guiar nuestro analizador con representaciones de
los movimientos oculares.
En general, los resultados muestran que la reformulación del análisis
sintáctico de dependencias como etiquetado de secuencias es un enfoque
viable, rápido y preciso, y ofrece una alternativa práctica para integrar
la sintaxis en las tareas de PLN.[Resumo]
Esta tese presenta novos métodos para reformular a análise sintáctica
de dependencias como unha tarefa de etiquetaxe secuencial, o que
supón unha alternativa viable aos enfoques tradicionais baseados en
transicións e grafos. Demóstrase que os analizadores de etiquetaxe secuencial
ofrecen varias vantaxes para a análise sintáctica de dependencias,
por exemplo (i) un bo equilibrio entre a precisión e a velocidade
de análise, (ii) a xenericidade que permite executar un analizador nun
software xenérico de etiquetaxe secuencial e (iii) a conectividade que
permite empregar a árbore de análise completa como características
para as tarefas posteriores.
O piar da análise sintáctica de dependencias como etiquetaxe secuencial
son as codificacións que serven como métodos de linealización para
transformar as árbores de dependencias en etiquetas discretas, de forma
que cada token dunha frase se asocia cunha etiqueta. Introducimos
tres familias de codificación que comprenden: (i) selección de núcleos,
(ii) codificacións baseadas en corchetes e (iii) codificacións baseadas en
transicións que se diferencian pola forma en que representan unha árbore
de dependencia como unha secuencia de etiquetas. Examinamos
empíricamente a viabilidade das codificacións e ofrecemos unha análise
das súas facetas.
Ademais, exploramos a viabilidade de aproveitar datos complementarios
externos para mellorar o rendemento da análise sintáctica. O noso
analizador sintáctico de etiquetaxe secuencial está dotado de dous tipos
de representacións. En primeiro lugar, explotamos a natureza complementaria
dos paradigmas de análise sintáctica de dependencias e constituíntes
e enriquecemos o analizador sintáctico con representacións de
ambas abstraccións sintácticas. En segundo lugar, empregamos datos
de procesamento da linguaxe humana para guiar o noso analizador con
representacións dos movementos oculares.
En xeral, os resultados mostran que a reformulación da análise sintáctico
de dependencias como etiquetaxe de secuencias é un enfoque
viable, rápido e preciso, e ofrece unha alternativa práctica para integrar
a sintaxe nas tarefas de PLN.This work has been carried out thanks to the funding from
the European Research Council (ERC), under the European Union’s
Horizon 2020 research and innovation programme (FASTPARSE, grant
agreement No 714150)
Neural Combinatory Constituency Parsing
東京都立大学Tokyo Metropolitan University博士(情報科学)doctoral thesi
Investigating multilingual approaches for parsing universal dependencies
Multilingual dependency parsing encapsulates any attempt to parse multiple languages. It can involve parsing multiple languages in isolation (poly-monolingual), leveraging training data from multiple languages to process any of the included languages (polyglot), or training on one or multiple languages to process a low-resource language with no training data (zero-shot). In this thesis, we explore multilingual dependency parsing across all three paradigms, first analysing whether polyglot training on a number of source languages is beneficial for processing a target language in a zero-shot cross-lingual dependency parsing experiment using annotation projection. The results of this experiment show that polyglot training produces an overall trend of better results on the target language but a highly-related single source language can still be better for transfer.
We then look at the role of pretrained language models in processing a moderately low-resource language in Irish. Here, we develop our own monolingual Irish BERT model gaBERT from scratch and compare it to a number of multilingual baselines, showing that developing a monolingual language model for Irish is worthwhile. We then turn to the topic of parsing Enhanced Universal Dependencies (EUD) Graphs, which are an extension of basic Universal Dependencies trees, where we describe the DCU-EPFL submission to the 2021 IWPT shared task on EUD parsing. Here, we developed a multitask model to jointly learn the tasks of basic dependency parsing and EUD graph parsing, showing improvements over a single-task basic dependency parser. Lastly, we revisit the topic of polyglot parsing and investigate whether multiview learning can be applied to the problem of multilingual dependency parsing. Here, we learn different views based on the dataset source. We show that multiview learning can be used to train parsers with multiple datasets, showing a general improvement over single-view baselines
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Structured Learning with Inexact Search: Advances in Shift-Reduce CCG Parsing
Statistical shift-reduce parsing involves the interplay of representation learning, structured learning, and inexact search. This dissertation considers approaches that tightly integrate these three elements and explores three novel models for shift-reduce CCG parsing. First, I develop a dependency model, in which the selection of shift-reduce action sequences producing a dependency structure is treated as a hidden variable; the key components of the model are a dependency oracle and a learning algorithm that integrates the dependency oracle, the structured perceptron, and beam search. Second, I present expected F-measure training and show how to derive a globally normalized RNN model, in which beam search is naturally incorporated and used in conjunction with the
objective to learn shift-reduce action sequences optimized for the final evaluation metric. Finally, I describe an LSTM model that is able to construct parser state representations incrementally by following the shift-reduce syntactic derivation process; I show expected F-measure training, which is agnostic to the underlying neural network, can be applied in this setting to obtain globally normalized greedy and beam-search LSTM shift-reduce parsers.The Carnegie Trust for the Universities of Scotland;
The Cambridge Trus
Statistical Parsing by Machine Learning from a Classical Arabic Treebank
Research into statistical parsing for English has enjoyed over a decade of successful results. However, adapting these models to other languages has met with difficulties. Previous comparative work has shown that Modern Arabic is one of the most difficult languages to parse due to rich morphology and free word order. Classical Arabic is the ancient form of Arabic, and is understudied in computational linguistics, relative to its worldwide reach as the language of the Quran. The thesis is based on seven publications that make significant contributions to knowledge relating to annotating and parsing Classical Arabic.
Classical Arabic has been studied in depth by grammarians for over a thousand years using a traditional grammar known as i’rāb (إعغاة ). Using this grammar to develop a representation for parsing is challenging, as it describes syntax using a hybrid of phrase-structure and dependency relations. This work aims to advance the state-of-the-art for hybrid parsing by introducing a formal representation for annotation and a resource for machine learning. The main contributions are the first treebank for Classical Arabic and the first statistical dependency-based parser in any language for ellipsis, dropped pronouns and hybrid representations.
A central argument of this thesis is that using a hybrid representation closely aligned to traditional grammar leads to improved parsing for Arabic. To test this hypothesis, two approaches are compared. As a reference, a pure dependency parser is adapted using graph transformations, resulting in an 87.47% F1-score. This is compared to an integrated parsing model with an F1-score of 89.03%, demonstrating that joint dependency-constituency parsing is better suited to Classical Arabic.
The Quran was chosen for annotation as a large body of work exists providing detailed syntactic analysis. Volunteer crowdsourcing is used for annotation in combination with expert supervision. A practical result of the annotation effort is the corpus website: http://corpus.quran.com, an educational resource with over two million users per year
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