Mineração de Mapas Conceituais a partir de Textos em Português

Aguiar, Camila; Cury, Davidson

doi:10.5753/cbie.sbie.2017.1117

Cited by 2 publications

(4 citation statements)

References 3 publications

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“…This section will present the preliminary results of implementing the proposed methodology, reconstructing the experiments described in [Aguiar and Cury 2017]. We draw a comparative analysis between the CMs produced by humans, the results reported by the authors, and those obtained through our approach, to highlight our methodology's effectiveness and potential within the same experimentation framework.…”

Section: Results and Implicationsmentioning

confidence: 99%

“…To encapsulate the scale of CMs produced, we provide a summary in Tab. 1, through which it is possible to observe that: the MC obtained from the fusion of the five CMs generated by human participants comprises 63 concepts and 137 propositions; CM produced by the methodology proposed by [Aguiar and Cury 2017] encapsulates 80 concepts and 123 propositions; in contrast, the CM processed by our approach contains 51 concepts and 43 propositions.…”

Section: Figura 5 Generated CM From Our Approachmentioning

confidence: 94%

“…Leveraging the merging algorithm proposed by [Vassoler et al 2014], we generated a collective CM encompassing all propositions identified by the five participants. This merged map then served as the basis for comparison against the results presented in [Aguiar and Cury 2017] and those yielded by our approach. Fig.…”

Section: Figura 4 Comparison Of Experiments Settingsmentioning

confidence: 99%

“…This result indicates that our method was more accurate Tabela 1. Generated CMs Summary Generated CMs Summary Merged [Aguiar et al, 2017] Concepts on [Aguiar et al, 2017] approach Concepts on Our approach Concepts on precision 0,488 0,603 Merged recall 0,603 0,619 Propositions on [Aguiar et al, 2017] approach Propositions on Our approach Propositions on precision 0,358 0,791 Merged recall 0,307 0,409 in identifying true positives and minimizing false positives, thus demonstrating a higher quality of results.…”

Section: Figura 5 Generated CM From Our Approachmentioning

confidence: 99%

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From Text to Maps: Automated Concept Map Generation Using Fine-tuned Large Language Model

Perin,

Cury,

Aguiar

et al. 2023

Anais Do XXXIV Simpósio Brasileiro De Informática Na Educação (SBIE 2023)

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Concept maps (CMs) are tools for visualizing relationships between ideas, facilitating more effective comprehension and learning. However, the automatic generation of CMs from unstructured text presents a challenge, often requiring semantic markup and subsequent complex processing. This paper introduces a novel approach to address this hurdle by harnessing the capabilities of fine-tuned Large Language Models (LLMs). Our innovative methodology uses these models to extract structured propositions from unstructured text, subsequently serving as the foundation for constructing a CM. This process reverses the transformation of CM relations into first-order logic propositions, a concept explored in our previous work. To achieve this, we train the LLM using fine-tuning techniques, leveraging the latest advancements in artificial intelligence and machine learning. We evaluate our proposed solution based on precision and recall metrics, comparing our outcomes against models crafted by experts. Notably, the results indicate that our method can contribute significantly to advancements in the automatic generation of CMs, illustrating another application bolstered by recent breakthroughs in artificial intelligence. As a stepping stone in this promising direction, future research should continue to refine the model and explore potential applications across diverse domains.

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Section: Results and Implicationsmentioning

confidence: 99%

Section: Figura 5 Generated CM From Our Approachmentioning

confidence: 94%

Section: Figura 4 Comparison Of Experiments Settingsmentioning

confidence: 99%

Section: Figura 5 Generated CM From Our Approachmentioning

confidence: 99%

See 2 more Smart Citations

From Text to Maps: Automated Concept Map Generation Using Fine-tuned Large Language Model

Perin,

Cury,

Aguiar

et al. 2023

Anais Do XXXIV Simpósio Brasileiro De Informática Na Educação (SBIE 2023)

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Text mining in education

Mello

Andre

Pinheiro

et al. 2019

WIREs Data Min & Knowl

148

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The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently, we have not found any paper surveying them. In this line, this work presents a systematic overview of the current status of the Educational Text Mining field. Our final goal is to answer three main research questions: Which are the text mining techniques most used in educational environments? Which are the most used educational resources? And which are the main applications or educational goals? Finally, we outline the conclusions and the more interesting future trends. This article is categorized under: Application Areas > Education and Learning Ensemble Methods > Text Mining

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Mineração de Mapas Conceituais a partir de Textos em Português

Cited by 2 publications

References 3 publications

From Text to Maps: Automated Concept Map Generation Using Fine-tuned Large Language Model

From Text to Maps: Automated Concept Map Generation Using Fine-tuned Large Language Model

Text mining in education

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