156 research outputs found
Memory-Based Shallow Parsing
We present memory-based learning approaches to shallow parsing and apply
these to five tasks: base noun phrase identification, arbitrary base phrase
recognition, clause detection, noun phrase parsing and full parsing. We use
feature selection techniques and system combination methods for improving the
performance of the memory-based learner. Our approach is evaluated on standard
data sets and the results are compared with that of other systems. This reveals
that our approach works well for base phrase identification while its
application towards recognizing embedded structures leaves some room for
improvement
Example-based machine translation of the Basque language
Basque is both a minority and a highly inflected language with free order of sentence constituents. Machine Translation of Basque is thus both a real need and a test bed for MT techniques. In this paper, we present a modular Data-Driven MT system which includes different chunkers as well as chunk aligners which can deal with the free order of sentence constituents of Basque. We conducted Basque to English translation experiments, evaluated on a large corpus
(270, 000 sentence pairs). The experimental results show that our system significantly outperforms state-of-the-art
approaches according to several common automatic evaluation metrics
Introduction to the CoNLL-2000 Shared Task: Chunking
We describe the CoNLL-2000 shared task: dividing text into syntactically
related non-overlapping groups of words, so-called text chunking. We give
background information on the data sets, present a general overview of the
systems that have taken part in the shared task and briefly discuss their
performance.Comment: 6 page
Tagging Complex Non-Verbal German Chunks with Conditional Random Fields
We report on chunk tagging methods for German that recognize complex non-verbal phrases using structural chunk tags with Conditional Random Fields (CRFs). This state-of-the-art method for sequence classification achieves 93.5% accuracy on newspaper text. For the same task, a classical trigram tagger approach based on Hidden Markov Models reaches a baseline of 88.1%. CRFs allow for a clean and principled integration of linguistic knowledge such as part-of-speech tags, morphological constraints and lemmas. The structural chunk tags encode phrase structures up to a depth of 3 syntactic nodes. They include complex prenominal and postnominal modifiers that occur frequently in German noun phrases
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Proceedings of QG2010: The Third Workshop on Question Generation
These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge".
QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)
Hungarian noun phrase extraction using rule-based and hybrid methods
We implement and revise Kornai's grammar of Hungarian NPs [11] to create a parser that identifies noun phrases in Hungarian text. After making several practical amendments to our morphological annotation system of choice, we proceed to formulate rules to account for some specific phenomena of the Hungarian language not covered by the original rule system. Although the performance of the final parser is still inferior to state-of-the-art machine learning methods, we use its output successfully to improve the performance of one such system
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
Question Answering System using Open Source Software
To get the answers of a question there are lots of search engine availbale. But the problem with the search engine is that instead of giving a straight forrward answer they usually gives the links/URL to the webpages which might have the answers. Instead of providing links Question Answering System will provide the straighforward answer to user\u27s question. Tasks intended by the project : • Takes question as an input from the user. • Analyses the sentiment behinds the question. • Lookover the information availbale in the knowledge base related to question. • Compute the answer of the question from the knowledge base. • Present the answer to the user if it is available. Strightforward answer will be very useful and time efficient and it is really helpful for the users who are using small screen devices, since in those devices it is very hard to find answers in webpage with lots of irrelevant content
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