813 research outputs found
Improving the Arc-Eager Model with Reverse Parsing
A known way to improve the accuracy of dependency parsers is to combine several different parsing algorithms, in such a way that the weaknesses of each of the models can be compensated by the strengths of others. For example, voting-based combination schemes are based on variants of the idea of analyzing each sentence with various parsers, and constructing a combined output where the head of each node is determined by "majority vote" among the different parsers. Typically, such approaches combine very different parsing models to take advantage of the variability in the parsing errors they make. In this paper, we show that consistent improvements in accuracy can be obtained in a much simpler way by combining a single parser with itself. In particular, we start with a greedy implementation of the Nivre pseudo-projective arc-eager algorithm, a well-known left-to-right transition-based parser, and we combine it with a "mirrored" version of the algorithm that analyzes sentences from right to left. To determine which of the two obtained outputs we trust for the head of each node, we use simple criteria based on the length and position of dependency arcs. Experiments on several datasets from the CoNLL-X shared task and the WSJ section of the English Penn Treebank show that the novel combination system obtains better performance than the baseline arc-eager parser in all cases. To test the generality of the approach, we also perform experiments with a different transition system (arc-standard) and a different search strategy (beam search), obtaining similar improvements in all these settings
Improvements to the performance and applicability of dependency parsing
[Resumen]Los analizadores de dependencias han generado un gran interés en las últimas décadas
debido a su utilidad en un amplio rango de tareas de procesamiento de lenguaje natural.
Estos utilizan grafos de dependencias para definir la estructura sintáctica de una oración
dada. En particular, los algoritmos basados en transiciones proveen un análisis sintáctico
de dependencias eficiente y preciso. Sin embargo, su principal inconveniente es que
tienden a sufrir propagación de errores. AsÃ, una decisión temprana tomada erróneamente
podrÃa posicionar el analizador en un estado incorrecto, causando más errores en futuras
decisiones.
Esta tesis se centra en mejorar la precisión de los analizadores basados en transiciones
mediante la reducción del efecto de la propagación de errores, mientras mantienen su
velocidad y eficiencia. Concretamente, proponemos cinco enfoques diferentes que han
demostrado ser beneficiosos para su rendimiento, al aliviar la propagación de errores e
incrementar su precisión.
Además, hemos ampliado la utilidad de los analizadores de dependencias más allá
de la construcción de grafos de dependencias. Presentamos una novedosa técnica que
permite que estos sean capaces de construir representaciones de constituyentes. Esto
cubrirÃa la necesidad de la comunidad de procesamiento de lenguaje natural de disponer
de un analizador eficiente capaz de proveer un árbol de constituyentes para representar la
estructura sintáctica de las oraciones.[Abstract]Dependency parsers have attracted a remarkable interest in the last two decades due
to their usefulness in a wide range of natural language processing tasks. They employ
a dependency graph to define the syntactic structure of a given sentence. In particular,
transition-based algorithms provide accurate and efficient dependency syntactic analyses.
However, the main drawback of these techniques is that they tend to suffer from error
propagation. So, an early erroneous decision may place the parser into an incorrect state,
causing more errors in future decisions.
This thesis focuses on improving the accuracy of transition-based parsers by reducing
the effect of error propagation, while preserving their speed and efficiency. Concretely,
we propose five different approaches that proved to be beneficial for their performance,
mitigating the presence of error propagation and boosting its accuracy.
We also extend the usefulness of dependency parsers beyond building dependency
graphs.We present a novel technique that allows these to build constituent representations.
This meets the necessity of the natural language processing community to have an
efficient parser able to provide constituent trees to represent the syntactic structure of
sentences.[Resumo]Os analizadores de dependencias xeraron gran interese nas últimas décadas debido
á súa utilidade nun amplo rango de tarefas de procesamento da linguaxe natural. Estes
utilizan grafos de dependencias para definir a estrutura sintáctica dunha oración dada.
En particular, os algoritmos baseados en transicións provén un análise sintáctico de
dependencias eficiente e preciso. Sen embargo, o seu principal inconveniente é que tenden
a sufrir propagación de erros. AsÃ, unha decisión temprana tomada erroneamente poderÃa
posicionar o analizador nun estado incorrecto, causando máis erros en futuras decisións.
Esta tese centrase en mellorar a precisión dos analizadores baseados en transicións
mediante a redución do efecto da propagación de erros, mentres manteñen a súa
velocidade e eficiencia. Concretamente, propomos cinco diferentes enfoques que
demostraron ser beneficiosos para o seu rendemento, ó aliviar a propagación de erros
e incrementar a súa precisión.
Ademais, ampliámo-la utilidade dos analizadores de dependencias máis alá da
construción de grafos de dependencias. Presentamos unha novidosa técnica que permite
que estes sexan capaces de construir representacións de constituÃntes. Isto cubrirÃa a
necesidade da comunidade de procesamento da linguaxe natural de dispor dun analizador
eficiente capaz de prover unha árbore de constituÃntes para representar a estrutura
sintáctica das oracións
Scene Graph Parsing as Dependency Parsing
In this paper, we study the problem of parsing structured knowledge graphs from textual descrip- tions. In particular, we consider the scene graph representation that considers objects together with their attributes and relations: this representation has been proved useful across a variety of vision and language applications. We begin by introducing an alternative but equivalent edge-centric view of scene graphs that connect to dependency parses. Together with a careful redesign of label and action space, we combine the two-stage pipeline used in prior work (generic dependency parsing followed by simple post-processing) into one, enabling end-to-end training. The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49.67% to ground truth graphs on our evaluation set, surpassing best previous approaches by 5%. We further demonstrate the effective- ness of our learned parser on image retrieval applications.This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216
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 lÃneas 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 teorÃas 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 lÃnea 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 lÃnea 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 teorÃa 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 avalÃa 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
Scene Graph Parsing as Dependency Parsing
In this paper, we study the problem of parsing structured knowledge graphs
from textual descriptions. In particular, we consider the scene graph
representation that considers objects together with their attributes and
relations: this representation has been proved useful across a variety of
vision and language applications. We begin by introducing an alternative but
equivalent edge-centric view of scene graphs that connect to dependency parses.
Together with a careful redesign of label and action space, we combine the
two-stage pipeline used in prior work (generic dependency parsing followed by
simple post-processing) into one, enabling end-to-end training. The scene
graphs generated by our learned neural dependency parser achieve an F-score
similarity of 49.67% to ground truth graphs on our evaluation set, surpassing
best previous approaches by 5%. We further demonstrate the effectiveness of our
learned parser on image retrieval applications.Comment: To appear in NAACL 2018 as oral. Code is available at
https://github.com/Yusics/bist-parser/tree/sgparse
Cache Transition Systems for Graph Parsing
Motivated by the task of semantic parsing, we describe a transition system that generalizes standard transition-based dependency parsing techniques to generate a graph rather than a tree. Our system includes a cache with fixed size m, and we characterize the relationship between the parameter m and the class of graphs that can be produced through the graph-theoretic concept of tree decomposition. We find empirically that small cache sizes cover a high percentage of sentences in existing semantic corpora
Automatic inference of causal reasoning chains from student essays
While there has been an increasing focus on higher-level thinking skills arising from the Common Core Standards, many high-school and middle-school students struggle to combine and integrate information from multiple sources when writing essays. Writing is an important learning skill, and there is increasing evidence that writing about a topic develops a deeper understanding in the student. However, grading essays is time consuming for teachers, resulting in an increasing focus on shallower forms of assessment that are easier to automate, such as multiple-choice tests. Existing essay grading software has attempted to ease this burden but relies on shallow lexico-syntactic features and is unable to understand the structure or validity of a student’s arguments or explanations. Without the ability to understand a student’s reasoning processes, it is impossible to write automated formative assessment systems to assist students with improving their thinking skills through essay writing.
In order to understand the arguments put forth in an explanatory essay in the science domain, we need a method of representing the causal structure of a piece of explanatory text. Psychologists use a representation called a causal model to represent a student\u27s understanding of an explanatory text. This consists of a number of core concepts, and a set of causal relations linking them into one or more causal chains, forming a causal model. In this thesis I present a novel system for automatically constructing causal models from student scientific essays using Natural Language Processing (NLP) techniques.
The problem was decomposed into 4 sub-problems - assigning essay concepts to words, detecting causal-relations between these concepts, resolving coreferences within each essay, and using the structure of the whole essay to reconstruct a causal model. Solutions to each of these sub-problems build upon the predictions from the solutions to earlier problems, forming a sequential pipeline of models. Designing a system in this way allows later models to correct for false positive predictions from downstream models. However, this also has the disadvantage that errors made in earlier models can propagate through the system, negatively impacting the upstream models, and limiting their accuracy. Producing robust solutions for the initial 2 sub problems, detecting concepts, and parsing causal relations between them, was critical in building a robust system.
A number of sequence labeling models were trained to classify the concepts associated with each word, with the most effective approach being a bidirectional recurrent neural network (RNN), a deep learning model commonly applied to word labeling problems. This is because the RNN used pre-trained word embeddings to better generalize to rarer words, and was able to use information from both ends of each sentence to infer a word\u27s concept. The concepts predicted by this model were then used to develop causal relation parsing models for detecting causal connections between these concepts. A shift-reduce dependency parsing model was trained using the SEARN algorithm and out-performed a number of other approaches by better utilizing the structure of the problem and directly optimizing the error metric used.
Two pre-trained coreference resolution systems were used to resolve coreferences within the essays. However a word tagging model trained to predict anaphors combined with a heuristic for determining the antecedent out-performed these two systems. Finally, a model was developed for parsing a causal model from an entire essay, utilizing the solutions to the three previous problems. A beam search algorithm was used to produce multiple parses for each sentence, which in turn were combined to generate multiple candidate causal models for each student essay. A reranking algorithm was then used to select the optimal causal model from all of the generated candidates.
An important contribution of this work is that it represents a system for parsing a complete causal model of a scientific essay from a student\u27s written answer. Existing systems have been developed to parse individual causal relations, but no existing system attempts to parse a sequence of linked causal relations forming a causal model from an explanatory scientific essay. It is hoped that this work can lead to the development of more robust essay grading software and formative assessment tools, and can be extended to build solutions for extracting causality from text in other domains. In addition, I also present 2 novel approaches for optimizing the micro-F1 score within the design of two of the algorithms studied: the dependency parser and the reranking algorithm. The dependency parser uses a custom cost function to estimate the impact of parsing mistakes on the overall micro-F1 score, while the reranking algorithm allows the micro-F1 score to be optimized by tuning the beam search parameter to balance recall and precision
- …