Aprendizaje por refuerzo de un parser semántico óptimo en DRT

Este documento se trata del procesamiento de lenguaje natural (NLP, por sus siglas en inglés), que se enfoca en desarrollar sistemas de comunicación efectivos entre computadoras y humanos. Aunque los mayores avances en esta área se han logrado mediante grandes modelos de lenguaje (LLMs, por sus sigl...

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Fecha de publicación:
2024
Institución:
Universidad del Rosario
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Repositorio EdocUR - U. Rosario
Idioma:
spa
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oai:repository.urosario.edu.co:10336/43268
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https://doi.org/10.48713/10336_43268
https://repository.urosario.edu.co/handle/10336/43268
Palabra clave:
Representación formal del lenguaje
Razonamiento automático
Inferencia Lógica
Teoría de la Representación del Discurso
Procesamiento de Lenguaje Natural
Formal representation of language
Automatic reasoning
Logical inference
Discourse representation theory
Natural language processing
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dc.title.none.fl_str_mv Aprendizaje por refuerzo de un parser semántico óptimo en DRT
dc.title.TranslatedTitle.none.fl_str_mv Reinforcement Learning of an Optimal Semantic Parser in DRT
title Aprendizaje por refuerzo de un parser semántico óptimo en DRT
spellingShingle Aprendizaje por refuerzo de un parser semántico óptimo en DRT
Representación formal del lenguaje
Razonamiento automático
Inferencia Lógica
Teoría de la Representación del Discurso
Procesamiento de Lenguaje Natural
Formal representation of language
Automatic reasoning
Logical inference
Discourse representation theory
Natural language processing
title_short Aprendizaje por refuerzo de un parser semántico óptimo en DRT
title_full Aprendizaje por refuerzo de un parser semántico óptimo en DRT
title_fullStr Aprendizaje por refuerzo de un parser semántico óptimo en DRT
title_full_unstemmed Aprendizaje por refuerzo de un parser semántico óptimo en DRT
title_sort Aprendizaje por refuerzo de un parser semántico óptimo en DRT
dc.contributor.advisor.none.fl_str_mv Andrade Lotero, Édgar José
dc.subject.none.fl_str_mv Representación formal del lenguaje
Razonamiento automático
Inferencia Lógica
Teoría de la Representación del Discurso
Procesamiento de Lenguaje Natural
topic Representación formal del lenguaje
Razonamiento automático
Inferencia Lógica
Teoría de la Representación del Discurso
Procesamiento de Lenguaje Natural
Formal representation of language
Automatic reasoning
Logical inference
Discourse representation theory
Natural language processing
dc.subject.keyword.none.fl_str_mv Formal representation of language
Automatic reasoning
Logical inference
Discourse representation theory
Natural language processing
description Este documento se trata del procesamiento de lenguaje natural (NLP, por sus siglas en inglés), que se enfoca en desarrollar sistemas de comunicación efectivos entre computadoras y humanos. Aunque los mayores avances en esta área se han logrado mediante grandes modelos de lenguaje (LLMs, por sus siglas en inglés), estos suelen ser imprecisos en dominios regidos por reglas, como las relaciones espaciales o las normas legales. Para abordar estos dominios, se utilizan parsers semánticos que asignan representaciones lógicas a los textos a través del análisis de su estructura sintáctica y la interpretación semántica. Sin embargo, estos parsers son complejos y su diseño es complicado debido a la implementación manual de reglas específicas. Este estudio propone un enfoque innovador que utiliza el aprendizaje por refuerzo profundo para desarrollar un parser semántico que pueda aprender y adaptarse automáticamente. El agente, a través de recompensas, optimizará su comportamiento con el tiempo, lo que podría tener un impacto significativo en el avance del procesamiento de lenguaje natural.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-08-13T20:32:42Z
dc.date.available.none.fl_str_mv 2024-08-13T20:32:42Z
dc.date.created.none.fl_str_mv 2024-08-12
dc.type.none.fl_str_mv bachelorThesis
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.document.none.fl_str_mv Trabajo de grado
dc.type.spa.none.fl_str_mv Trabajo de grado
dc.identifier.doi.none.fl_str_mv https://doi.org/10.48713/10336_43268
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/43268
url https://doi.org/10.48713/10336_43268
https://repository.urosario.edu.co/handle/10336/43268
dc.language.iso.none.fl_str_mv spa
language spa
dc.rights.*.fl_str_mv Attribution-NonCommercial-ShareAlike 4.0 International
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dc.rights.acceso.none.fl_str_mv Abierto (Texto Completo)
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
rights_invalid_str_mv Attribution-NonCommercial-ShareAlike 4.0 International
Abierto (Texto Completo)
http://creativecommons.org/licenses/by-nc-sa/4.0/
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dc.format.extent.none.fl_str_mv 44 PP
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad del Rosario
dc.publisher.department.spa.fl_str_mv Escuela de Ingeniería, Ciencia y Tecnología
dc.publisher.program.spa.fl_str_mv Maestría en Matemáticas Aplicadas y Ciencias de la Computación
institution Universidad del Rosario
dc.source.bibliographicCitation.none.fl_str_mv Eisenstein, Jacob (2019) Introduction to Natural Language Processing. : MIT Press;
Davidson, Donald (2001) Essays on Actions and Events: Philosophical Essays Volume 1. Oxford, GB: Clarendon Press;
Kamp, H; Reyle, U (1993) From Discourse to Logic. Dordrecht: Kluwer;
Geurts, Bart; Beaver, David I; Maier, Emar (2020) Discourse Representation Theory. : Metaphysics Research Lab, Stanford University; Disponible en: https://plato.stanford.edu/archives/spr2020/entries/discourse-representation-theory/.
van Noord, Rik; Abzianidze, Lasha; Toral, Antonio; Bos, Johan (2018) Exploring Neural Methods for Parsing Discourse Representation Structures. En: Transactions of the Association for Computational Linguistics. Vol. 6; pp. 619-633 Cambridge, MA: MIT Press; Disponible en: https://aclanthology.org/Q18-1043; http://dx.doi.org/10.1162/tacl_a_00241. Disponible en: 10.1162/tacl_a_00241.
Bos, Johan (2008) Wide-Coverage Semantic Analysis with Boxer. En: Semantics in Text Processing. STEP 2008 Conference Proceedings. pp. 277-286 : College Publications; Disponible en: https://aclanthology.org/W08-2222.
van Lambalgen, Michiel; Hamm, Fritz (2008) The Proper Treatment of Events. En: Explorations in Semantics.: Wiley; 9780470759226;
Andrade-Lotero, Edgar (2006) Meaning and Form in Event Calculus. MSc. Thesis. : ILLC, Universiteit van Amsterdam;
Jurafsky, Daniel; Martin, James (2008) Speech and Language Processing. : Prentice Hall;
van Harmelen, Frank; Lifschitz, Vladimir; Porter, Bruce (2008) Handbook of Knowledge Representation. Amsterdam: Elsevier;
Vaswani, Ashish; Shazeer, Noam; Parmar, Niki; Uszkoreit, Jakob; Jones, Llion; Gomez, Aidan N; Kaiser, Lukasz; Polosukhin, Illia (2023) Attention Is All You Need. En: arXiv [cs.CL]. Disponible en: http://arxiv.org/abs/1706.03762.
Traylor, Aaron; Feiman, Roman; Pavlick, Ellie; Zong, Chengqing; Xia, Fei; Li, Wenjie; Navigli, Roberto (2021) AND does not mean OR: Using Formal Languages to Study Language Models'. En: Proceedings of the 59th Annual Meeting of the Association for. pp. 158-167 : Association for Computational Linguistics;
Kassner, Nora; Krojer, Benno; Schütze, Hinrich; Fernández, Raquel; Linzen, Tal (2020) Are Pretrained Language Models Symbolic Reasoners over Knowledge?. En: Proceedings of the 24th Conference on Computational Natural Language. pp. 552-564 : Association for Computational Linguistics;
Basmov, Victoria; Goldberg, Yoav; Tsarfaty, Reut (2024) Simple Linguistic Inferences of Large Language Models (LLMs): Blind Spots. En: arXiv [cs.CL]. Disponible en: http://arxiv.org/abs/2305.14785.
Minsky, Marvin; Winston, P H (1975) A framework for representing knowledge. En: The Psychology of Computer Vision.: McGraw-Hill;
McDermott, D V (1987) A critique of pure reason. En: Computational Intelligence. Vol. 3; pp. 151-160
Gamut, L T F (1991) Logic, Language and Meaning Vol. 2. : University of Chicago Press;
Mueller, Erik T (2006) Common Sense Reasoning. : Elsevier;
Sutton, Richard S; Barto, Andrew G (2018) Reinforcement Learning. : MIT Press;
Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Graves, Alex; Antonoglou, Ioannis; Wierstra, Daan; Riedmiller, Martin A (2013) Playing Atari with Deep Reinforcement Learning. En: CoRR. Vol. abs/1312.5602;
Abzianidze, Lasha; Bjerva, Johannes; Evang, Kilian; Haagsma, Hessel; van Noord, Rik; Ludmann, Pierre; Nguyen, Duc-Duy; Bos, Johan (2017) The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations. En: Proceedings of the 15th Conference of the European Chapter of the. pp. 242-247 : Association for Computational Linguistics; Disponible en: https://aclanthology.org/E17-2039.
Zai, Alexander; Brown, Brandon (2020) Deep Reinforcement Learning in Action. : Manning Publications;
Ozdemir, Sinan (2023) Quick Start Guide to Large Language Models: Strategies and Best Practices. : Addison-Wesley Professional;
Montague, Richard (1974) Formal Philosophy: Selected Papers of Richard Montague. : New Haven: Yale University Press;
Hugging Face (2024) sentence-transformers/distiluse-base-multilingual-cased-v1. Disponible en: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1.
Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Others, (2015) Human-level control through deep reinforcement learning. En: Nature. Vol. 518; No. 7540; pp. 529-533 : Nature Publishing Group;
Binz, Marcel; Schulz, Eric (2023) Using cognitive psychology to understand GPT-3. En: Proceedings of the National Academy of Sciences. Vol. 120; No. 6; Proceedings of the National Academy of Sciences; 1091-6490; Disponible en: http://dx.doi.org/10.1073/pnas.2218523120. Disponible en: 10.1073/pnas.2218523120.
Uc-Cetina, Víctor; Navarro-Guerrero, Nicolás; Martin-Gonzalez, Anabel; Weber, Cornelius; Wermter, Stefan (2022) Survey on reinforcement learning for language processing. En: Artificial Intelligence Review. Vol. 56; No. 2; pp. 1543–1575 : Springer Science and Business Media LLC; 1573-7462; Disponible en: http://dx.doi.org/10.1007/s10462-022-10205-5. Disponible en: 10.1007/s10462-022-10205-5.
Schulman, John; Wolski, Filip; Dhariwal, Prafulla; Radford, Alec; Klimov, Oleg (2017) Proximal Policy Optimization Algorithms. En: arXiv [cs.LG]. Disponible en: http://arxiv.org/abs/1707.06347.
Starc, Janez; Mladenić, Dunja (2016) Joint learning of ontology and semantic parser from text. En: arXiv [cs.AI]. Disponible en: http://arxiv.org/abs/1601.00901.
Yang, Zhilin; Qi, Peng; Zhang, Saizheng; Bengio, Yoshua; Cohen, William W; Salakhutdinov, Ruslan; Manning, Christopher D (2018) HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. En: arXiv [cs.CL]. Disponible en: http://arxiv.org/abs/1809.09600.
Geva, Mor; Khashabi, Daniel; Segal, Elad; Khot, Tushar; Roth, Dan; Berant, Jonathan (2021) Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit. En: Transactions of the Association for Computational Linguistics. Vol. 9; pp. 346-361 Disponible en: https://api.semanticscholar.org/CorpusID:230799347.
Ho, Xanh; Duong Nguyen, Anh-Khoa; Sugawara, Saku; Aizawa, Akiko; Scott, Donia; Bel, Nuria; Zong, Chengqing (2020) Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of. En: Proceedings of the 28th International Conference on Computational. pp. 6609-6625 : International Committee on Computational Linguistics; Disponible en: https://aclanthology.org/2020.coling-main.580; http://dx.doi.org/10.18653/v1/2020.coling-main.580. Disponible en: 10.18653/v1/2020.coling-main.580.
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spelling Andrade Lotero, Édgar José95ab7599-4981-49d0-a5f4-110463e87e54Piza Londoño, JesseniaMagíster en Matemáticas Aplicadas y Ciencias de la ComputaciónFull time5f0b3042-5a76-4b2f-9c53-ea82325dcc7c-12024-08-13T20:32:42Z2024-08-13T20:32:42Z2024-08-12Este documento se trata del procesamiento de lenguaje natural (NLP, por sus siglas en inglés), que se enfoca en desarrollar sistemas de comunicación efectivos entre computadoras y humanos. Aunque los mayores avances en esta área se han logrado mediante grandes modelos de lenguaje (LLMs, por sus siglas en inglés), estos suelen ser imprecisos en dominios regidos por reglas, como las relaciones espaciales o las normas legales. Para abordar estos dominios, se utilizan parsers semánticos que asignan representaciones lógicas a los textos a través del análisis de su estructura sintáctica y la interpretación semántica. Sin embargo, estos parsers son complejos y su diseño es complicado debido a la implementación manual de reglas específicas. Este estudio propone un enfoque innovador que utiliza el aprendizaje por refuerzo profundo para desarrollar un parser semántico que pueda aprender y adaptarse automáticamente. El agente, a través de recompensas, optimizará su comportamiento con el tiempo, lo que podría tener un impacto significativo en el avance del procesamiento de lenguaje natural.This document is about natural language processing (NLP), which focuses on developing effective communication systems between computers and humans. While the most significant advances in this area have been achieved through large language models (LLMs), these models often lack precision in rule-governed domains, such as spatial relations or legal norms. To address these domains, semantic parsers are used to assign logical representations to texts by analyzing their syntactic structure and semantic interpretation. However, these parsers are complex, and their design is challenging due to the manual implementation of specific rules. This study proposes an innovative approach using deep reinforcement learning to develop a semantic parser that can learn and adapt automatically. Through rewards, the agent will optimize its behavior over time, which could have a significant impact on the advancement of natural language processing.44 PPapplication/pdfhttps://doi.org/10.48713/10336_43268 https://repository.urosario.edu.co/handle/10336/43268spaUniversidad del RosarioEscuela de Ingeniería, Ciencia y TecnologíaMaestría en Matemáticas Aplicadas y Ciencias de la ComputaciónAttribution-NonCommercial-ShareAlike 4.0 InternationalAbierto (Texto Completo)http://creativecommons.org/licenses/by-nc-sa/4.0/http://purl.org/coar/access_right/c_abf2Eisenstein, Jacob (2019) Introduction to Natural Language Processing. : MIT Press;Davidson, Donald (2001) Essays on Actions and Events: Philosophical Essays Volume 1. Oxford, GB: Clarendon Press;Kamp, H; Reyle, U (1993) From Discourse to Logic. Dordrecht: Kluwer;Geurts, Bart; Beaver, David I; Maier, Emar (2020) Discourse Representation Theory. : Metaphysics Research Lab, Stanford University; Disponible en: https://plato.stanford.edu/archives/spr2020/entries/discourse-representation-theory/.van Noord, Rik; Abzianidze, Lasha; Toral, Antonio; Bos, Johan (2018) Exploring Neural Methods for Parsing Discourse Representation Structures. En: Transactions of the Association for Computational Linguistics. Vol. 6; pp. 619-633 Cambridge, MA: MIT Press; Disponible en: https://aclanthology.org/Q18-1043; http://dx.doi.org/10.1162/tacl_a_00241. Disponible en: 10.1162/tacl_a_00241.Bos, Johan (2008) Wide-Coverage Semantic Analysis with Boxer. En: Semantics in Text Processing. STEP 2008 Conference Proceedings. pp. 277-286 : College Publications; Disponible en: https://aclanthology.org/W08-2222.van Lambalgen, Michiel; Hamm, Fritz (2008) The Proper Treatment of Events. En: Explorations in Semantics.: Wiley; 9780470759226;Andrade-Lotero, Edgar (2006) Meaning and Form in Event Calculus. MSc. Thesis. : ILLC, Universiteit van Amsterdam;Jurafsky, Daniel; Martin, James (2008) Speech and Language Processing. : Prentice Hall; van Harmelen, Frank; Lifschitz, Vladimir; Porter, Bruce (2008) Handbook of Knowledge Representation. Amsterdam: Elsevier;Vaswani, Ashish; Shazeer, Noam; Parmar, Niki; Uszkoreit, Jakob; Jones, Llion; Gomez, Aidan N; Kaiser, Lukasz; Polosukhin, Illia (2023) Attention Is All You Need. En: arXiv [cs.CL]. Disponible en: http://arxiv.org/abs/1706.03762.Traylor, Aaron; Feiman, Roman; Pavlick, Ellie; Zong, Chengqing; Xia, Fei; Li, Wenjie; Navigli, Roberto (2021) AND does not mean OR: Using Formal Languages to Study Language Models'. En: Proceedings of the 59th Annual Meeting of the Association for. pp. 158-167 : Association for Computational Linguistics;Kassner, Nora; Krojer, Benno; Schütze, Hinrich; Fernández, Raquel; Linzen, Tal (2020) Are Pretrained Language Models Symbolic Reasoners over Knowledge?. En: Proceedings of the 24th Conference on Computational Natural Language. pp. 552-564 : Association for Computational Linguistics;Basmov, Victoria; Goldberg, Yoav; Tsarfaty, Reut (2024) Simple Linguistic Inferences of Large Language Models (LLMs): Blind Spots. En: arXiv [cs.CL]. Disponible en: http://arxiv.org/abs/2305.14785.Minsky, Marvin; Winston, P H (1975) A framework for representing knowledge. En: The Psychology of Computer Vision.: McGraw-Hill;McDermott, D V (1987) A critique of pure reason. En: Computational Intelligence. Vol. 3; pp. 151-160 Gamut, L T F (1991) Logic, Language and Meaning Vol. 2. : University of Chicago Press;Mueller, Erik T (2006) Common Sense Reasoning. : Elsevier;Sutton, Richard S; Barto, Andrew G (2018) Reinforcement Learning. : MIT Press;Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Graves, Alex; Antonoglou, Ioannis; Wierstra, Daan; Riedmiller, Martin A (2013) Playing Atari with Deep Reinforcement Learning. En: CoRR. Vol. abs/1312.5602;Abzianidze, Lasha; Bjerva, Johannes; Evang, Kilian; Haagsma, Hessel; van Noord, Rik; Ludmann, Pierre; Nguyen, Duc-Duy; Bos, Johan (2017) The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations. En: Proceedings of the 15th Conference of the European Chapter of the. pp. 242-247 : Association for Computational Linguistics; Disponible en: https://aclanthology.org/E17-2039.Zai, Alexander; Brown, Brandon (2020) Deep Reinforcement Learning in Action. : Manning Publications;Ozdemir, Sinan (2023) Quick Start Guide to Large Language Models: Strategies and Best Practices. : Addison-Wesley Professional;Montague, Richard (1974) Formal Philosophy: Selected Papers of Richard Montague. : New Haven: Yale University Press;Hugging Face (2024) sentence-transformers/distiluse-base-multilingual-cased-v1. Disponible en: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1.Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Others, (2015) Human-level control through deep reinforcement learning. En: Nature. Vol. 518; No. 7540; pp. 529-533 : Nature Publishing Group;Binz, Marcel; Schulz, Eric (2023) Using cognitive psychology to understand GPT-3. En: Proceedings of the National Academy of Sciences. Vol. 120; No. 6; Proceedings of the National Academy of Sciences; 1091-6490; Disponible en: http://dx.doi.org/10.1073/pnas.2218523120. Disponible en: 10.1073/pnas.2218523120.Uc-Cetina, Víctor; Navarro-Guerrero, Nicolás; Martin-Gonzalez, Anabel; Weber, Cornelius; Wermter, Stefan (2022) Survey on reinforcement learning for language processing. En: Artificial Intelligence Review. Vol. 56; No. 2; pp. 1543–1575 : Springer Science and Business Media LLC; 1573-7462; Disponible en: http://dx.doi.org/10.1007/s10462-022-10205-5. Disponible en: 10.1007/s10462-022-10205-5.Schulman, John; Wolski, Filip; Dhariwal, Prafulla; Radford, Alec; Klimov, Oleg (2017) Proximal Policy Optimization Algorithms. En: arXiv [cs.LG]. Disponible en: http://arxiv.org/abs/1707.06347.Starc, Janez; Mladenić, Dunja (2016) Joint learning of ontology and semantic parser from text. En: arXiv [cs.AI]. Disponible en: http://arxiv.org/abs/1601.00901.Yang, Zhilin; Qi, Peng; Zhang, Saizheng; Bengio, Yoshua; Cohen, William W; Salakhutdinov, Ruslan; Manning, Christopher D (2018) HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. En: arXiv [cs.CL]. Disponible en: http://arxiv.org/abs/1809.09600.Geva, Mor; Khashabi, Daniel; Segal, Elad; Khot, Tushar; Roth, Dan; Berant, Jonathan (2021) Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit. En: Transactions of the Association for Computational Linguistics. Vol. 9; pp. 346-361 Disponible en: https://api.semanticscholar.org/CorpusID:230799347.Ho, Xanh; Duong Nguyen, Anh-Khoa; Sugawara, Saku; Aizawa, Akiko; Scott, Donia; Bel, Nuria; Zong, Chengqing (2020) Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of. En: Proceedings of the 28th International Conference on Computational. pp. 6609-6625 : International Committee on Computational Linguistics; Disponible en: https://aclanthology.org/2020.coling-main.580; http://dx.doi.org/10.18653/v1/2020.coling-main.580. 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