372 research outputs found
Extraction automatique de paraphrases à partir de petits corpus
International audienceThis paper presents a versatile system intended to acquire paraphrastic phrases from a small-size representative corpus. In order to decrease the time spent on the elaboration of resources for NLP system (for example for Information Extraction), we suggest to use a knowledge acquisition module that helps extracting new information despite linguistic variation. This knowledge is semi-automatically derived from the text collection, in interaction with a large semantic network.Cet article présente un système permettant d'acquérir de manière semi-automatique des paraphrases à partir de corpus représentatifs de petite taille. Afin de réduire le temps passé à l'élaboration de ressources pour des systèmes de traitement des langues (notamment l'extraction d'information), nous décrivons un module qui vise à extraire ces connaissances en prenant en compte la variation linguistique. Les connaissances sont directement extraites des textes à l'aide d'un réseau sémantique de grande taille
The Circle of Meaning: From Translation to Paraphrasing and Back
The preservation of meaning between inputs and outputs is perhaps
the most ambitious and, often, the most elusive goal of systems
that attempt to process natural language. Nowhere is this goal of
more obvious importance than for the tasks of machine translation
and paraphrase generation. Preserving meaning between the input and
the output is paramount for both, the monolingual vs bilingual distinction
notwithstanding. In this thesis, I present a novel, symbiotic relationship
between these two tasks that I term the "circle of meaning''.
Today's statistical machine translation (SMT) systems require high
quality human translations for parameter tuning, in addition to
large bi-texts for learning the translation units. This parameter
tuning usually involves generating translations at different points
in the parameter space and obtaining feedback against human-authored
reference translations as to how good the translations. This feedback
then dictates what point in the parameter space should be explored
next. To measure this feedback, it is generally considered wise to have
multiple (usually 4) reference translations to avoid unfair penalization of translation
hypotheses which could easily happen given the large number of ways in which
a sentence can be translated from one language to another. However, this reliance on multiple reference translations
creates a problem since they are labor intensive and expensive to obtain.
Therefore, most current MT datasets only contain a single reference.
This leads to the problem of reference sparsity---the primary open problem
that I address in this dissertation---one that has a serious effect on the
SMT parameter tuning process.
Bannard and Callison-Burch (2005) were the first to provide a practical
connection between phrase-based statistical machine translation and paraphrase
generation. However, their technique is restricted to generating phrasal
paraphrases. I build upon their approach and augment a phrasal paraphrase
extractor into a sentential paraphraser with extremely broad coverage.
The novelty in this augmentation lies in the further strengthening of
the connection between statistical machine translation and paraphrase
generation; whereas Bannard and Callison-Burch only relied on SMT machinery
to extract phrasal paraphrase rules and stopped there, I take it a few
steps further and build a full English-to-English SMT system. This system
can, as expected, ``translate'' any English input sentence into a new English
sentence with the same degree of meaning preservation that exists in a bilingual
SMT system. In fact, being a state-of-the-art SMT system, it is able to generate
n-best "translations" for any given input sentence. This sentential
paraphraser, built almost entirely from existing SMT machinery, represents
the first 180 degrees of the circle of meaning.
To complete the circle, I describe a novel connection in the other direction.
I claim that the sentential paraphraser, once built in this fashion, can
provide a solution to the reference sparsity problem and, hence, be used
to improve the performance a bilingual SMT system. I discuss two different
instantiations of the sentential paraphraser and show several results that
provide empirical validation for this connection
16th International NooJ 2022 Conference: Book of Abstracts
Libro de resúmenes presentados en la "16th International NooJ 2022 Conference", de modalidad híbrida, realizada en el ECU (Espacio Cultural Universitario, UNR) en Rosario, Santa Fe, Argentina, entre el 14 y 15 de junio de 2022.Fil: Reyes, Silvia Susana. Universidad Nacional de Rosario. Facultad de Humanidades y Artes; Argentin
Lexical simplification for the systematic support of cognitive accessibility guidelines
The Internet has come a long way in recent years, contributing to the proliferation of
large volumes of digitally available information. Through user interfaces we can access
these contents, however, they are not accessible to everyone. The main users affected are
people with disabilities, who are already a considerable number, but accessibility barriers
affect a wide range of user groups and contexts of use in accessing digital information.
Some of these barriers are caused by language inaccessibility when texts contain long
sentences, unusual words and complex linguistic structures. These accessibility barriers
directly affect people with cognitive disabilities.
For the purpose of making textual content more accessible, there are initiatives such
as the Easy Reading guidelines, the Plain Language guidelines and some of the languagespecific
Web Content Accessibility Guidelines (WCAG). These guidelines provide documentation,
but do not specify methods for meeting the requirements implicit in these
guidelines in a systematic way. To obtain a solution, methods from the Natural Language
Processing (NLP) discipline can provide support for achieving compliance with the cognitive
accessibility guidelines for the language.
The task of text simplification aims at reducing the linguistic complexity of a text from
a syntactic and lexical perspective, the latter being the main focus of this Thesis. In this
sense, one solution space is to identify in a text which words are complex or uncommon,
and in the case that there were, to provide a more usual and simpler synonym, together
with a simple definition, all oriented to people with cognitive disabilities.
With this goal in mind, this Thesis presents the study, analysis, design and development
of an architecture, NLP methods, resources and tools for the lexical simplification of
texts for the Spanish language in a generic domain in the field of cognitive accessibility.
To achieve this, each of the steps present in the lexical simplification processes is studied,
together with methods for word sense disambiguation. As a contribution, different
types of word embedding are explored and created, supported by traditional and dynamic
embedding methods, such as transfer learning methods. In addition, since most of the
NLP methods require data for their operation, a resource in the framework of cognitive
accessibility is presented as a contribution.Internet ha avanzado mucho en los últimos años contribuyendo a la proliferación de
grandes volúmenes de información disponible digitalmente. A través de interfaces de
usuario podemos acceder a estos contenidos, sin embargo, estos no son accesibles a todas
las personas. Los usuarios afectados principalmente son las personas con discapacidad
siendo ya un número considerable, pero las barreras de accesibilidad afectan a un gran
rango de grupos de usuarios y contextos de uso en el acceso a la información digital. Algunas
de estas barreras son causadas por la inaccesibilidad al lenguaje cuando los textos
contienen oraciones largas, palabras inusuales y estructuras lingüísticas complejas. Estas
barreras de accesibilidad afectan directamente a las personas con discapacidad cognitiva.
Con el fin de hacer el contenido textual más accesible, existen iniciativas como las
pautas de Lectura Fácil, las pautas de Lenguaje Claro y algunas de las pautas de Accesibilidad
al Contenido en la Web (WCAG) específicas para el lenguaje. Estas pautas
proporcionan documentación, pero no especifican métodos para cumplir con los requisitos
implícitos en estas pautas de manera sistemática. Para obtener una solución, los
métodos de la disciplina del Procesamiento del Lenguaje Natural (PLN) pueden dar un
soporte para alcanzar la conformidad con las pautas de accesibilidad cognitiva relativas al
lenguaje
La tarea de la simplificación de textos del PLN tiene como objetivo reducir la complejidad
lingüística de un texto desde una perspectiva sintáctica y léxica, siendo esta última
el enfoque principal de esta Tesis. En este sentido, un espacio de solución es identificar
en un texto qué palabras son complejas o poco comunes, y en el caso de que sí hubiera,
proporcionar un sinónimo más usual y sencillo, junto con una definición sencilla, todo
ello orientado a las personas con discapacidad cognitiva.
Con tal meta, en esta Tesis, se presenta el estudio, análisis, diseño y desarrollo de
una arquitectura, métodos PLN, recursos y herramientas para la simplificación léxica de
textos para el idioma español en un dominio genérico en el ámbito de la accesibilidad
cognitiva. Para lograr esto, se estudia cada uno de los pasos presentes en los procesos
de simplificación léxica, junto con métodos para la desambiguación del sentido de las
palabras. Como contribución, diferentes tipos de word embedding son explorados y creados,
apoyados por métodos embedding tradicionales y dinámicos, como son los métodos
de transfer learning. Además, debido a que gran parte de los métodos PLN requieren
datos para su funcionamiento, se presenta como contribución un recurso en el marco de
la accesibilidad cognitiva.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Antonio Macías Iglesias.- Secretario: Israel González Carrasco.- Vocal: Raquel Hervás Ballestero
Abstract syntax as interlingua: Scaling up the grammatical framework from controlled languages to robust pipelines
Syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate generation and translation, as well as grammar engineering tools and components for mobile and Web applications. On the research side, the focus in the last ten years has been on scaling up GF to wide-coverage language processing. The concept of abstract syntax offers a unified view on many other approaches: Universal Dependencies, WordNets, FrameNets, Construction Grammars, and Abstract Meaning Representations. This makes it possible for GF to utilize data from the other approaches and to build robust pipelines. In return, GF can contribute to data-driven approaches by methods to transfer resources from one language to others, to augment data by rule-based generation, to check the consistency of hand-annotated corpora, and to pipe analyses into high-precision semantic back ends. This article gives an overview of the use of abstract syntax as interlingua through both established and emerging NLP applications involving GF
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