3 research outputs found
Models for Improved Tractability and Accuracy in Dependency Parsing
Automatic syntactic analysis of natural language is one of the fundamental problems in natural language processing. Dependency parses (directed trees in which edges represent the syntactic relationships between the words in a sentence) have been found to be particularly useful for machine translation, question answering, and other practical applications.
For English dependency parsing, we show that models and features compatible with how conjunctions are represented in treebanks yield a parser with state-of-the-art overall accuracy and substantial improvements in the accuracy of conjunctions.
For languages other than English, dependency parsing has often been formulated as either searching over trees without any crossing dependencies (projective trees) or searching over all directed spanning trees. The former sacrifices the ability to produce many natural language structures; the latter is NP-hard in the presence of features with scopes over siblings or grandparents in the tree.
This thesis explores alternative ways to simultaneously produce crossing dependencies in the output and use models that parametrize over multiple edges. Gap inheritance is introduced in this thesis and quantifies the nesting of subtrees over intervals. The thesis provides O(n6) and O(n5) edge-factored parsing algorithms for two new classes of trees based on this property, and extends the latter to include grandparent factors.
This thesis then defines 1-Endpoint-Crossing trees, in which for any edge that is crossed, all other edges that cross that edge share an endpoint. This property covers 95.8% or more of dependency parses across a variety of languages. A crossing-sensitive factorization introduced in this thesis generalizes a commonly used third-order factorization (capable of scoring triples of edges simultaneously).
This thesis provides exact dynamic programming algorithms that find the optimal 1-Endpoint-Crossing tree under either an edge-factored model or this crossing-sensitive third-order model in O(n4) time, orders of magnitude faster than other mildly non-projective parsing algorithms and identical to the parsing time for projective trees under the third-order model. The implemented parser is significantly more accurate than the third-order projective parser under many experimental settings and significantly less accurate on none
Interpretación tabular de autómatas para lenguajes de adjunción de árboles
[Resumen] Las gramáticas de adjunción de árboles son una extensión de las gramáticas independientes del
contexto que utilizan árboles en vez de producciones como estructuras elementales y que resultan
adecuadas para la descripción de la mayor parte de las construcciones sintácticas presentes en el
lenguaje natural. Los lenguajes generados por esta clase de gramáticas se denominan lenguajes
de adjunción de árboles y son equivalentes a los lenguajes generados por las gramáticas lineales
de Ãndices y otros formalismos suavemente dependientes del contexto.
En la primera parte de esta memoria se presenta el problema del análisis sintáctico de los
lenguajes de adjunción de árboles. Para ello, se establece un camino evolutivo continuo en el
que se sitúan los algoritmos de análisis sintáctico que incorporan las estrategias de análisis más
importantes, tanto para el caso de las gramáticas de adjunción de árboles como para el caso de
las gramáticas lineales de Ãndices.
En la segunda parte se definen diferentes modelos de autómata que aceptan exactamente los
lenguajes de adjunción de árboles y se proponen técnicas que permiten su ejecución eficiente.
La utilización de autómatas para realizar el análisis sintáctico es interesante porque permite
separar el problema de la definición de un algoritmo de análisis sintáctico del problema de la
ejecución del mismo, al tiempo que simplifica las pruebas de corrección. Concretamente, hemos
estudiado los siguientes modelos de autómata:
• Los autómatas a pila embebidos descendentes y ascendentes, dos extensiones de ^ los
autómatas a pila que utilizan como estructura de almacenamiento una pila de pilas. Hemos
definido nuevas versiones de estos autómatas en las cuales se simplifica la forma de
las transiciones y se elimina el control de estado finito, manteniendo la potencia expresiva.
• La restricción de los autómatas lógicos a pila para adaptarlos al reconocimiento de las
gramáticas lineales de Ãndices, obteniéndose diferentes tipos de autómatas especializados
en diversas estrategias de análisis según el conjunto de transiciones permitido.
• Los autómatas lineales de Ãndices, tanto los orientados a la derecha, adecuados para estrategias
en las cuales las adjunciones se reconocen de manera ascendente, los orientados a la
izquierda, aptos para estrategias de análisis en las que las adjunciones se tratan de forma
descendente, como los fuertemente dirigidos, capaces de incorporar estrategias de análisis
en las cuales las adjunciones se tratan de manera ascendente y/o descendente.
• Los autómatas con dos pilas, una extensión de los autómatas a pila que trabaja con una
pila maestra encargada de dirigir el proceso de análisis y una pila auxiliar que restringe
las transiciones aplicables en un momento dado. Hemos descrito dos versiones diferentes
de este tipo de autómatas, los autómatas con dos pilas fuertemente dirigidos, aptos para
describir estrategias de análisis arbitrarias, y los autómatas con dos pilas ascendentes,
adecuados para describir estrategias de análisis en las cuales las adjunciones se procesan ascendentemente.
Hemos definido esquemas de compilación para todos estos modelos de autómata. Estos
esquemas permiten obtener el conjunto de transiciones correspondiente a la implantación de
una determinada estrategia de análisis sintáctico para una gramática dada.
Todos los modelos de autómata pueden ser ejecutados en tiempo polinomial con respecto a
la longitud de la cadena de entrada mediante la aplicación de técnicas de interpretación tabular.
Estas técnicas se basan en la manipulación de representaciones colapsadas de las configuraciones
del autómata, denominadas Ãtems, que se almacenan en una tabla para su posterior reutilización.
Con ello se evita la realización de cálculos redundantes.
Finalmente, hemos analizado conjuntamente los diferentes modelos de autómata, los cuales
se pueden dividir en tres grandes grupos: la familia de los autómatas generales, de la que
forman parte los autómatas lineales de Ãndices fuertemente dirigidos y los autómatas con dos
pilas fuertemente dirigidos; la familia de los autómatas descendentes, en la que se encuadran
los autómatas a pila embebidos y los autómatas lineales de Ãndices orientados a la izquierda; y
la familia de los autómatas ascendentes, en la que se enmarcan los autómatas a pila embebidos
ascendentes, los autómatas lineales de Ãndices orientados a la derecha y los autómatas con dos
pilas ascendentes.[Abstract] Tree adjoining grammars are an extension of context-free grammars that use trees instead of
productions as the primary representing structure and that are considered to be adequate to
describe most of syntactic phenomena occurring in natural languages. These grammars generate
the class of tree adjoining languages, which is equivalent to the class of languages generated by
linear indexed grammars and other mildly context-sensitive formalisms.
In the first part of this dissertation, we introduce the problem of parsing tree adjoining
grammars and linear indexed grammars, creating, for both formalisms, a continuum from simple
pure bottom-up algorithms to complex predictive algorithms and showing what transformations
must be applied to each one in order to obtain the next one in the continuum.
In the second part, we define several models of automata that accept the class of tree adjoining
languages, proposing techniques for their efficient execution. The use of automata for
parsing is interesting because they allow us to separate the problem of the definition of parsing
algorithms from the problem of their execution. We have considered the following types of
automata:
• Top-down and bottom-up embedded push-down automata, two extensions of push-down
automata working on nested stacks. A new definition is provided in which the finite-state
control has been eliminated and several kinds of normalized transition have been defined,
preserving the equivalence with tree adjoining languages.
• Logical push-down automata restricted to the case of tree adjoining languages. Depending
on the set of allowed transitions, we obtain three different types of automata.
• Linear indexed automata, left-oriented and right-oriented to describe parsing strategies
in which adjuntions are recognized top-down and bottom-up, respectively, and stronglydriven
to define parsing strategies recognizing adjunctions top-down and/or bottom-up.
• 2-stack automata, an extension of push-down automata working on a pair of stacks, a
master stack driving the parsing process and an auxiliary stack restricting the set of
transitions that can be applied at a given moment. Strongly-driven 2-stack automata can
be used to describe bottom-up, top-down or mixed parsing strategies for tree adjoining
languages with respect to the recognition of the adjunctions. Bottom-up 2-stack automata
are specifically designed for parsing strategies recognizing adjunctions bottom-up.
Compilation schemata for these models of automata have been defined. A compilation
schema allow us to obtain the set of transitions corresponding to the implementation of a^ parsing
strategy for a given grammar.
All the presented automata can be executed in polynomial time with respect to the length
of the input string by applying tabulation techniques. A tabular technique makes possible to
interpret an automaton by means of the manipulation of collapsed representation of configurations
(called items) instead of actual configurations. Items are stored into a table in order to be
reused, avoiding redundant computations.
Finally, we have studied the relations among the diÃferent classes of automata, the main
dif%rence being the storage structure used: embedded stacks, indices lists or coupled stacks.
According to the strategies that can be implemented, we can distinguish three kinds of automata:
bottom-up automata, including bottom-up embedded push-down automata, bottomup
restricted logic push-down automata, right-oriented linear indexed automata and bottom-up
2-stack automata; top-down automata, including (top-down) embedded push-down automata,
top-down restricted logic push-down automata and left-oriented linear indexed automata; and
general automata, including strongly-driven linear indexed automata and strongly-driven 2-
stack automata
Restrictions on Tree Adjoining Languages
Several methods are known for parsing languages generated by Tree Adjoining Grammars (TAGs) in O(n6) worst case running time. In this paper we investigate which restrictions on TAGs and TAG derivations are needed in order to lower this O(n6) time complexity, without introducing large runtime constants, and without losing any of the generative power needed to capture the syntactic constructions in natural language that can be handled by unrestricted TAGs. In particular, we describe an algorithm for parsing a strict subcalss of TAG in O(n5), and attempt to show that this subclass retains enough generative power to make it useful in the general case