144 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
Improving Syntactic Parsing of Clinical Text Using Domain Knowledge
Syntactic parsing is one of the fundamental tasks of Natural Language Processing (NLP). However, few studies have explored syntactic parsing in the medical domain. This dissertation systematically investigated different methods to improve the performance of syntactic parsing of clinical text, including (1) Constructing two clinical treebanks of discharge summaries and progress notes by developing annotation guidelines that handle missing elements in clinical sentences; (2) Retraining four state-of-the-art parsers, including the Stanford parser, Berkeley parser, Charniak parser, and Bikel parser, using clinical treebanks, and comparing their performance to identify better parsing approaches; and (3) Developing new methods to reduce syntactic ambiguity caused by Prepositional Phrase (PP) attachment and coordination using semantic information.
Our evaluation showed that clinical treebanks greatly improved the performance of existing parsers. The Berkeley parser achieved the best F-1 score of 86.39% on the MiPACQ treebank. For PP attachment, our proposed methods improved the accuracies of PP attachment by 2.35% on the MiPACQ corpus and 1.77% on the I2b2 corpus. For coordination, our method achieved a precision of 94.9% and a precision of 90.3% for the MiPACQ and i2b2 corpus, respectively. To further demonstrate the effectiveness of the improved parsing approaches, we applied outputs of our parsers to two external NLP tasks: semantic role labeling and temporal relation extraction. The experimental results showed that performance of both tasks’ was improved by using the parse tree information from our optimized parsers, with an improvement of 3.26% in F-measure for semantic role labelling and an improvement of 1.5% in F-measure for temporal relation extraction
Natural Language Processing Resources for Finnish. Corpus Development in the General and Clinical Domains
Siirretty Doriast
Proceedings
Proceedings of the Ninth International Workshop
on Treebanks and Linguistic Theories.
Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti.
NEALT Proceedings Series, Vol. 9 (2010), 268 pages.
© 2010 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/15891
A Computational Model Of Cognitive Constraints In Syntactic Locality
This dissertation is broadly concerned with the question: how do human cognitive limitations influence difficult sentences? The focus is a class of grammatical restrictions, locality constraints. The majority of relations between words are local; the relations between question words and their governors are not. Locality constraints restrict the formation of these non-local dependencies. Though necessary, the origin, operation, and scope of locality constraints is a controversial topic in the literature. The dissertation describes the implementation of a computational model that clarifies these issues. The model tests, against behavioral data, a series of cognitive constraints argued to account for locality. The result is an explanatory model predictive of a variety of cross-linguistic locality data. The model distinguishes those cognitive limitations that affect locality processing, and addresses the competence-performance debate by determining how and when cognitive constraints explain human behavior. The results provide insight into the nature of locality constraints, and promote language models sensitive to human cognitive limitations
One Model to Rule them all: Multitask and Multilingual Modelling for Lexical Analysis
When learning a new skill, you take advantage of your preexisting skills and
knowledge. For instance, if you are a skilled violinist, you will likely have
an easier time learning to play cello. Similarly, when learning a new language
you take advantage of the languages you already speak. For instance, if your
native language is Norwegian and you decide to learn Dutch, the lexical overlap
between these two languages will likely benefit your rate of language
acquisition. This thesis deals with the intersection of learning multiple tasks
and learning multiple languages in the context of Natural Language Processing
(NLP), which can be defined as the study of computational processing of human
language. Although these two types of learning may seem different on the
surface, we will see that they share many similarities.
The traditional approach in NLP is to consider a single task for a single
language at a time. However, recent advances allow for broadening this
approach, by considering data for multiple tasks and languages simultaneously.
This is an important approach to explore further as the key to improving the
reliability of NLP, especially for low-resource languages, is to take advantage
of all relevant data whenever possible. In doing so, the hope is that in the
long term, low-resource languages can benefit from the advances made in NLP
which are currently to a large extent reserved for high-resource languages.
This, in turn, may then have positive consequences for, e.g., language
preservation, as speakers of minority languages will have a lower degree of
pressure to using high-resource languages. In the short term, answering the
specific research questions posed should be of use to NLP researchers working
towards the same goal.Comment: PhD thesis, University of Groninge
Rapid Resource Transfer for Multilingual Natural Language Processing
Until recently the focus of the Natural Language Processing (NLP)
community has been on a handful of mostly European languages. However, the
rapid changes taking place in the economic and political climate of the
world precipitate a similar change to the relative importance given to
various languages. The importance of rapidly acquiring NLP resources and
computational capabilities in new languages is widely accepted.
Statistical NLP models have a distinct advantage over rule-based methods
in achieving this goal since they require far less manual labor. However,
statistical methods require two fundamental resources for training: (1)
online corpora (2) manual annotations. Creating these two resources can be
as difficult as porting rule-based methods.
This thesis demonstrates the feasibility of acquiring both corpora and
annotations by exploiting existing resources for well-studied languages.
Basic resources for new languages can be acquired in a rapid and
cost-effective manner by utilizing existing resources cross-lingually.
Currently, the most viable method of obtaining online corpora is
converting existing printed text into electronic form using Optical
Character Recognition (OCR). Unfortunately, a language that lacks online
corpora most likely lacks OCR as well. We tackle this problem by taking an
existing OCR system that was desgined for a specific language and using
that OCR system for a language with a similar script. We present a
generative OCR model that allows us to post-process output from a
non-native OCR system to achieve accuracy close to, or better than, a
native one. Furthermore, we show that the performance of a native or
trained OCR system can be improved by the same method.
Next, we demonstrate cross-utilization of annotations on treebanks. We
present an algorithm that projects dependency trees across parallel
corpora. We also show that a reasonable quality treebank can be generated
by combining projection with a small amount of language-specific
post-processing. The projected treebank allows us to train a parser that
performs comparably to a parser trained on manually generated data
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