Spanish Morphosyntactic Disambiguator

1. Introduction
The written expression of an idea is not achieved only through
the simple combination of the different components of the
grammar based on a given syntax. Other factors take part in
the process, such as semantics and context. But it is obvious
that a first approach requires at least a correct syntactic analysis,
and for this it is necessary, from the computer-science point of
view, to obtain results similar to those obtainable by human
knowledge. In this work, a first approach is achieved by the
identification and then disambiguation of the elements that are
part of a sentence.
Traditionally, syntactic analysis requires a specialized
knowledge of the language, all the more so in the case of
Spanish, due to its wide range of variations which turn the
syntactic analysis into a task only for experts. From the
educational point of view, syntactic analysis is very useful to
help learn to distinguish the different symbols implied: on the
one hand, the correct combination of the elements by means of
the application of grammar rules, and on the other hand, the
incorporation of less tangible, although necessary aspects, like
semantics and context. People usually perform an intuitive use
that hides the true difficulty of the problem.
This system is intended to provide a close view of the Spanish
grammar to researchers, enhancing their performance and reliability. This is a first step that will allow, with the addition
of new features, to keep improving until reaching 100%
accuracy. Any automated processing of a text entails inevitably
the syntactic analysis of its sentences, following the
morphosyntactic disambiguation of the elements that compose
it, allowing for different possible applications: a) to provide a
precise synonym for a given word, b) to analyze its literary
style, c) to reveal its semantics, d) to extract information or
summarize its contents, e) to make trustworthy translations to
other languages, f) to answer to concrete questions on its
content, etc.
2. Methodology
In this work, the number of erroneous syntactic representation
trees, obtained by the application of the rules of the Spanish
grammar by means of a set of structural disambiguation rules,
is notably reduced. In spite of the remarkable amount of
necessary combinations, this system does not limit itself to
subgroups of the grammar like most of the other proposals, but
instead it uses a system of rules which covers all the possible
combinations of the Spanish grammar. In addition to being the
starting point for an automated syntactic analysis system, it
complements the local functional disambiguator developed by
the Group of Data Structures and Computational Linguistics
of the University of Las Palmas de Gran Canaria ( <http:/
/www.gedlc.ulpgc.es/investigacion/desambi
gua/desambigua.htm> ). As an indicator of its
performance, the accuracy of the disambiguation is raised from
87% to 96%.
A solution is provided to the problem of the appearance of
structural ambiguities that are generated during the process of
construction of syntactic representation trees. The syntactic
structures are combined to each other to allow for the syntactic
representation trees. Many of these combinations generate
erroneous trees. Direct conflicts between rules have been
identified as one of the main causes of the problem. The
characteristics of the different syntactic structures and how they
must be considered at the time of accepting or not the
construction of a representation symbol have been studied for
the development of methods of structural disambiguation.
In view of the great number of possible combinations of the
grammar elements (more evident in verb-phrase constructions
which allow any number of elements and almost in any
combination), the adequate representation mechanisms have
been defined so that all the possibilities are covered, not leaving
valid options unrepresented. When allowing any combination
of possible elements in the verb-phrase, some combinations
appear, which should not be allowed, and would be rejected in
the structural disambiguation processes. In this way, all the
possible combinations are represented, from a structural point
of view, and those not allowed are rejected.
Groups of semantic identification oriented to the recognition
of syntactic structures are catalogued. The processes of
structural disambiguation include some rules that introduce
semantic information. The generated lists have been obtained
from the tables of the ideological dictionaries that can be related
to certain syntactic structures.
3. Knowledge base
The grammar used is based mainly on the description made by
Gili Gaya. To achieve maximum system completeness and
include all the syntactic structures that can appear we followed
Gutiérrez Araus. The examples cited by Gómez Torrego (2002a,
2002b), were useful to test the system and contributed mainly
to illustrate the aspects relative to the compound sentences that
remained to be refined.
For this work, the tagger developed by GEDLC was used ( <h
ttp://www.gedlc.ulpgc.es/investigacion/sc
ogeme02/lematiza.htm> ) which gathers the main
lexicographical repertoires of the Spanish language1, and admits
151103 canonical forms and something more than 4900000
inflectioned and derived forms (without adding the inherent
extension to the prefixes and the enclitic pronouns that have
also been contemplated).
4. Related works
There are other authors that approach this problem for the
Spanish language from diverse points of view. In the same way
as our work, which can be used for free at discretion through
the Internet ( <http://www.gedlc.ulpgc.es/inves
tigacion/desambigua/morfosintactico.htm> ),
we have only been able to find one other operative tool of this
kind on the network: the parser from the Center of Language
and Computing of the University of Barcelona. Given the high
complexity of the problem, they have chosen to write down
exclusively those elements that are explicitly present in the
sentence, which had led them to a simplified treatment of some
syntactic aspects like coordination and some subordinated types
that they leave unsolved. Also, they abandon the concept of
sentence understood like noun-phrase and verb-phrase, opting
for a list of components instead.
Although the computer methodologies applied are different,
they try to reach the same objectives. Our work is based on the
real and complete study of: a) a Spanish grammar that includes
all the possibilities available in the written language, b) the
direct structural ambiguities that cause the appearance of
multiple syntactic representation trees, c) the symbols that cannot cover all the sentence, d) the complex verbal form, e)
other situations where ambiguities can be solved based on
linguistic knowledge about words, grammar categories and
objects involved, and f) the considerations for the generation
of the predicate symbol. Nevertheless, other methodologies
apply statistical criteria for the resolution of ambiguities, with
the consequent loss of reliability for unfrequent cases. The
richness of our language and, particularly, the writers’ freedom
in the construction of syntactic structures makes us reconsider
the probabilistic methods as the only solution to this complex
problem.
5 Conclusions
This work is not limited to subsets of the grammar, but is based
instead on a system of rules for the Spanish grammar in spite
of the remarkable quantity of necessary combinations.
It provides a solution to the problem of the appearance of
functional ambiguities. First a disambiguation process is
applied, based on local syntactic structures that reach an
accuracy of 87%; and second, another disambiguation process
is applied, based on trees of syntactic representation that
improve the average accuracy level up to 96%.
The importance of this work lies on the fact that it fosters the
development of future applications, because:
1. It accelerates the process of syntactic analysis when pruning
incorrect structures.
2. It improves the precision in the results of advanced word
searches.
3. It allows the discarding of non valid options in information
extraction.
4. It detects grammatical errors in the written constructions.
1. Alvar Ezquerra; Casares; García Márquez & Hernández;
Diccionario General de la Lengua Española Vox; Gran Diccionario
de la Lengua Española; Gran Diccionario de Sinónimos y
Antónimos; Moline; Real Academia Española.
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