On the learnibility of Mildly Context-Sensitive languages using positive data and correction queries

With this dissertation, we bring together the Theory of the Grammatical Inference and Studies of language acquisition, in pursuit of our final goal: to go deeper in the understanding of the process of language acquisition by using the theory of inference of formal grammars. Our main three contributi...

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Detalles Bibliográficos
Autor: Becerra Bonache, Leonor
Tipo de recurso: tesis doctoral
Estado:Versión publicada
Fecha de publicación:2006
País:España
Institución:Universitat Rovira i virgili (URV)
Repositorio:Repositori Institucional de la Universitat Rovira i Virgili
OAI Identifier:oai:urv.cat:TDX:559
Acceso en línea:https://hdl.handle.net/20.500.11797/TDX559
http://hdl.handle.net/10803/8780
Access Level:acceso abierto
Palabra clave:81 - Lingüística i llengües
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dc.title.none.fl_str_mv On the learnibility of Mildly Context-Sensitive languages using positive data and correction queries
title On the learnibility of Mildly Context-Sensitive languages using positive data and correction queries
spellingShingle On the learnibility of Mildly Context-Sensitive languages using positive data and correction queries
Becerra Bonache, Leonor
81 - Lingüística i llengües
title_short On the learnibility of Mildly Context-Sensitive languages using positive data and correction queries
title_full On the learnibility of Mildly Context-Sensitive languages using positive data and correction queries
title_fullStr On the learnibility of Mildly Context-Sensitive languages using positive data and correction queries
title_full_unstemmed On the learnibility of Mildly Context-Sensitive languages using positive data and correction queries
title_sort On the learnibility of Mildly Context-Sensitive languages using positive data and correction queries
dc.creator.none.fl_str_mv Becerra Bonache, Leonor
author Becerra Bonache, Leonor
author_facet Becerra Bonache, Leonor
author_role author
dc.contributor.none.fl_str_mv Departament de Filologies Romàniques
Universitat Rovira i Virgili.
dc.subject.none.fl_str_mv 81 - Lingüística i llengües
topic 81 - Lingüística i llengües
description With this dissertation, we bring together the Theory of the Grammatical Inference and Studies of language acquisition, in pursuit of our final goal: to go deeper in the understanding of the process of language acquisition by using the theory of inference of formal grammars. Our main three contributions are:1. Introduction of a new class of languages called Simple p-dimensional external contextual (SEC). Despite the fact that the field of Grammatical Inference has focused its research on learning regular or context-free languages, we propose in our dissertation to focus these studies in classes of languages more relevant from a linguistic point of view (families of languages that occupy an orthogonal position in the Chomsky Hierarchy and are Mildly Context-Sensitive, for example SEC).2. Presentation of a new learning paradigm based on correction queries. One of the main results in the theory of formal learning is that deterministic finite automata (DFA) are efficiently learnable from membership query and equivalence query. Taken into account that in first language acquisition the correction of errors can play an important role, we have introduced in our dissertation a novel learning model by replacing membership queries with correction queries.3. Presentation of results based on the two previous contributions. First, we prove that SEC is learnable from only positive data. Second, we prove that it is possible to learn DFA from corrections and that the number of queries is reduced considerably.The results obtained with this dissertation suppose an important contribution to studies of Grammatical Inference (the current research in Grammatical Inference has focused mainly on the mathematical aspects of the models). Moreover, these results could be extended to studies relate
publishDate 2006
dc.date.none.fl_str_mv 2006
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/doctoralThesis
format doctoralThesis
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.11797/TDX559
http://hdl.handle.net/10803/8780
url https://hdl.handle.net/20.500.11797/TDX559
http://hdl.handle.net/10803/8780
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Rovira i Virgili
publisher.none.fl_str_mv Universitat Rovira i Virgili
dc.source.none.fl_str_mv urn:isbn:8469009761
reponame:Repositori Institucional de la Universitat Rovira i Virgili
instname:Universitat Rovira i virgili (URV)
instname_str Universitat Rovira i virgili (URV)
reponame_str Repositori Institucional de la Universitat Rovira i Virgili
collection Repositori Institucional de la Universitat Rovira i Virgili
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spelling On the learnibility of Mildly Context-Sensitive languages using positive data and correction queriesBecerra Bonache, Leonor81 - Lingüística i llengüesWith this dissertation, we bring together the Theory of the Grammatical Inference and Studies of language acquisition, in pursuit of our final goal: to go deeper in the understanding of the process of language acquisition by using the theory of inference of formal grammars. Our main three contributions are:1. Introduction of a new class of languages called Simple p-dimensional external contextual (SEC). Despite the fact that the field of Grammatical Inference has focused its research on learning regular or context-free languages, we propose in our dissertation to focus these studies in classes of languages more relevant from a linguistic point of view (families of languages that occupy an orthogonal position in the Chomsky Hierarchy and are Mildly Context-Sensitive, for example SEC).2. Presentation of a new learning paradigm based on correction queries. One of the main results in the theory of formal learning is that deterministic finite automata (DFA) are efficiently learnable from membership query and equivalence query. Taken into account that in first language acquisition the correction of errors can play an important role, we have introduced in our dissertation a novel learning model by replacing membership queries with correction queries.3. Presentation of results based on the two previous contributions. First, we prove that SEC is learnable from only positive data. Second, we prove that it is possible to learn DFA from corrections and that the number of queries is reduced considerably.The results obtained with this dissertation suppose an important contribution to studies of Grammatical Inference (the current research in Grammatical Inference has focused mainly on the mathematical aspects of the models). Moreover, these results could be extended to studies relateCon esta tesis doctoral aproximamos la teoría de la inferencia gramatical y los estudios de adquisición del lenguaje, en pos de un objetivo final: ahondar en la comprensión del modo como los niños adquieren su primera lengua mediante la explotación de la teoría inferencial de gramáticas formales.Nuestras tres principales aportaciones son:1. Introducción de una nueva clase de lenguajes llamada Simple p-dimensional external contextual (SEC). A pesar de que las investigaciones en inferencia gramatical se han centrado en lenguajes regulares o independientes del contexto, en nuestra tesis proponemos centrar esos estudios en clases de lenguajes más relevantes desde un punto de vista lingüístico (familias de lenguajes que ocupan una posición ortogonal en la jerarquía de Chomsky y que son suavemente dependientes del contexto, por ejemplo, SEC).2. Presentación de un nuevo paradigma de aprendizaje basado en preguntas de corrección. Uno de los principales resultados positivos dentro de la teoría del aprendizaje formal es el hecho de que los autómatas finitos deterministas (DFA) se pueden aprender de manera eficiente utilizando preguntas de pertinencia y preguntas de equivalencia. Teniendo en cuenta que en el aprendizaje de primeras lenguas la corrección de errores puede jugar un papel relevante, en nuestra tesis doctoral hemos introducido un nuevo modelo de aprendizaje que reemplaza las preguntas de pertinencia por preguntas de corrección.3. Presentación de resultados basados en las dos previas aportaciones. En primer lugar, demostramos que los SEC se pueden aprender a partir de datos positivos. En segundo lugar, demostramos que los DFA se pueden aprender a partir de correcciones y que el número de preguntas se reduce considerablemente.Los resultados obtenidos con esta tesis doctoUniversitat Rovira i Virgili Departament de Filologies RomàniquesUniversitat Rovira i Virgili.2006info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfhttps://hdl.handle.net/20.500.11797/TDX559http://hdl.handle.net/10803/8780urn:isbn:8469009761reponame:Repositori Institucional de la Universitat Rovira i Virgiliinstname:Universitat Rovira i virgili (URV)Inglésinfo:eu-repo/semantics/openAccessADVERTIMENT. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà  indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al repositori institucional de la Universitat Rovira i Virgili. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a aquest repositori (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs.oai:urv.cat:TDX:5592026-06-23T12:42:27Z
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