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2024
Journal article
Open Access
Explainable authorship identification in Cultural Heritage applications
Setzu M., Corbara S., Monreale A., Moreo Fernandez A., Sebastiani F.
While a substantial amount of work has recently been devoted to improving the accuracy of computational Authorship Identification (AId) systems for textual data, little to no attention has been paid to endowing AId systems with the ability to explain the reasons behind their predictions. This substantially hinders the practical application of AId methods, since the predictions returned by such systems are hardly useful unless they are supported by suitable explanations. In this article, we explore the applicability of existing general-purpose eXplainable Artificial Intelligence (XAI) techniques to AId, with a focus on explanations addressed to scholars working in cultural heritage. In particular, we assess the relative merits of three different types of XAI techniques (feature ranking, probing, factual and counterfactual selection) on three different AId tasks (authorship attribution, authorship verification and same-authorship verification) by running experiments on real AId textual data. Our analysis shows that, while these techniques make important first steps towards XAI, more work remains to be done to provide tools that can be profitably integrated into the workflows of scholars.Source: ACM JOURNAL ON COMPUTING AND CULTURAL HERITAGE, vol. 17 (issue 3), pp. 1-23
DOI: 10.1145/3654675
Project(s): SoBigData-PlusPlus
Metrics:
Setzu M., Corbara S., Monreale A., Moreo Fernandez A., Sebastiani F.
While a substantial amount of work has recently been devoted to improving the accuracy of computational Authorship Identification (AId) systems for textual data, little to no attention has been paid to endowing AId systems with the ability to explain the reasons behind their predictions. This substantially hinders the practical application of AId methods, since the predictions returned by such systems are hardly useful unless they are supported by suitable explanations. In this article, we explore the applicability of existing general-purpose eXplainable Artificial Intelligence (XAI) techniques to AId, with a focus on explanations addressed to scholars working in cultural heritage. In particular, we assess the relative merits of three different types of XAI techniques (feature ranking, probing, factual and counterfactual selection) on three different AId tasks (authorship attribution, authorship verification and same-authorship verification) by running experiments on real AId textual data. Our analysis shows that, while these techniques make important first steps towards XAI, more work remains to be done to provide tools that can be profitably integrated into the workflows of scholars.Source: ACM JOURNAL ON COMPUTING AND CULTURAL HERITAGE, vol. 17 (issue 3), pp. 1-23
DOI: 10.1145/3654675
Project(s): SoBigData-PlusPlus
Metrics:
See at:
CNR IRIS 
| Journal on Computing and Cultural Heritage 
| CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Forging the Forger: an attempt to improve authorship verification via data augmentation
Corbara S., Moreo Fernandez A.
Authorship Verification (AV) is a text classification task concerned with inferring whether a candidate text has been written by one specific author (A) or by someone else (A). It has been shown that many AV systems are vulnerable to adversarial attacks, where a malicious author actively tries to fool the classifier by either concealing their writing style, or by imitating the style of another author. In this paper, we investigate the potential benefits of augmenting the classifier training set with (negative) synthetic examples. These synthetic examples are generated to imitate the style of A. We analyze the improvements in the classifier predictions that this augmentation brings to bear in the task of AV in an adversarial setting. In particular, we experiment with three different generator architectures (one based on Recurrent Neural Networks, another based on small-scale transformers, and another based on the popular GPT model) and with two training strategies (one inspired by standard Language Models, and another inspired by Wasserstein Generative Adversarial Networks). We evaluate our hypothesis on five datasets (three of which have been specifically collected to represent an adversarial setting) and using two learning algorithms for the AV classifier (Support Vector Machines and Convolutional Neural Networks). This experimentation yields negative results, revealing that, although our methodology proves effective in many adversarial settings, its benefits are too sporadic for a pragmatical application.Source: IEEE ACCESS, vol. 12, pp. 171911-171925
DOI: 10.1109/access.2024.3481161
DOI: 10.48550/arxiv.2403.11265
Project(s): SoBigData-PlusPlus
Metrics:
Corbara S., Moreo Fernandez A.
Authorship Verification (AV) is a text classification task concerned with inferring whether a candidate text has been written by one specific author (A) or by someone else (A). It has been shown that many AV systems are vulnerable to adversarial attacks, where a malicious author actively tries to fool the classifier by either concealing their writing style, or by imitating the style of another author. In this paper, we investigate the potential benefits of augmenting the classifier training set with (negative) synthetic examples. These synthetic examples are generated to imitate the style of A. We analyze the improvements in the classifier predictions that this augmentation brings to bear in the task of AV in an adversarial setting. In particular, we experiment with three different generator architectures (one based on Recurrent Neural Networks, another based on small-scale transformers, and another based on the popular GPT model) and with two training strategies (one inspired by standard Language Models, and another inspired by Wasserstein Generative Adversarial Networks). We evaluate our hypothesis on five datasets (three of which have been specifically collected to represent an adversarial setting) and using two learning algorithms for the AV classifier (Support Vector Machines and Convolutional Neural Networks). This experimentation yields negative results, revealing that, although our methodology proves effective in many adversarial settings, its benefits are too sporadic for a pragmatical application.Source: IEEE ACCESS, vol. 12, pp. 171911-171925
DOI: 10.1109/access.2024.3481161
DOI: 10.48550/arxiv.2403.11265
Project(s): SoBigData-PlusPlus
Metrics:
See at:
arXiv.org e-Print Archive | IEEE Access | IEEE Access | Archivio istituzionale della Ricerca - Scuola Normale Superiore | CNR IRIS | ieeexplore.ieee.org | doi.org | GitHub | CNR IRIS
2024
Other
Open Access
AIMH Research Activities 2024
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Cassese M., Ciampi L., Coccomini D. A., Concordia C., Connor R., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Iannello L., Negi K., Lagani G., Lenzi E., Leocata M., Malvaldi M., Meghini C., Messina N., Moreo Fernandez A., Nardi A., Pacini G., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Scotti F., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L., Volpi L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is committed to advancing the field of Artificial Intelligence, with a special emphasis on its applications in digital media and the humanities. The lab aims to improve AI technologies, particularly in areas such as deep learning, text analysis, computer vision, multimedia information retrieval, content analysis, recognition, and retrieval. This report summarizes the laboratory’s achievements and activities over the course of 2024.DOI: 10.32079/isti-ar-2024/001
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Cassese M., Ciampi L., Coccomini D. A., Concordia C., Connor R., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Iannello L., Negi K., Lagani G., Lenzi E., Leocata M., Malvaldi M., Meghini C., Messina N., Moreo Fernandez A., Nardi A., Pacini G., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Scotti F., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L., Volpi L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is committed to advancing the field of Artificial Intelligence, with a special emphasis on its applications in digital media and the humanities. The lab aims to improve AI technologies, particularly in areas such as deep learning, text analysis, computer vision, multimedia information retrieval, content analysis, recognition, and retrieval. This report summarizes the laboratory’s achievements and activities over the course of 2024.DOI: 10.32079/isti-ar-2024/001
Metrics:
2023
Conference article
Open Access
Enhancing adversarial authorship verification with data augmentation
Corbara S, Moreo A
It has been shown that many Authorship Identification systems are vulnerable to adversarial attacks, where an author actively tries to fool the classifier. We propose to tackle the adversarial Authorship Verification task by augmenting the training set with synthetic textual examples. In this ongoing study, we present preliminary results using two learning algorithms (SVM and Neural Network), and two generation strategies (based on language modeling and GAN training) for two generator models, on three datasets. We empirically show that data augmentation may help improve the performance of the classifier in an adversarial setup.Source: CEUR WORKSHOP PROCEEDINGS, pp. 73-78. Pisa, Italy, 8-9/6/23.
Corbara S, Moreo A
It has been shown that many Authorship Identification systems are vulnerable to adversarial attacks, where an author actively tries to fool the classifier. We propose to tackle the adversarial Authorship Verification task by augmenting the training set with synthetic textual examples. In this ongoing study, we present preliminary results using two learning algorithms (SVM and Neural Network), and two generation strategies (based on language modeling and GAN training) for two generator models, on three datasets. We empirically show that data augmentation may help improve the performance of the classifier in an adversarial setup.Source: CEUR WORKSHOP PROCEEDINGS, pp. 73-78. Pisa, Italy, 8-9/6/23.
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2023
Other
Open Access
AIMH Research Activities 2023
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.DOI: 10.32079/isti-ar-2023/001
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.DOI: 10.32079/isti-ar-2023/001
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Journal article
Open Access
MedLatinEpi and MedLatinLit: two datasets for the computational authorship analysis of medieval Latin texts
Corbara S., Moreo Fernandez A., Sebastiani F., Tavoni M.
We present and make available MedLatinEpi and MedLatinLit, two datasets of medieval Latin texts to be used in research on computational authorship analysis. MedLatinEpi and MedLatinLit consist of 294 and 30 curated texts, respectively, labelled by author; MedLatinEpi texts are of epistolary nature, while MedLatinLit texts consist of literary comments and treatises about various subjects. As such, these two datasets lend themselves to supporting research in authorship analysis tasks, such as authorship attribution, authorship verification, or same-author verification. Along with the datasets we provide experimental results, obtained on these datasets, for the authorship verification task, i.e., the task of predicting whether a text of unknown authorship was written by a candidate author or not. We also make available the source code of the authorship verification system we have used, thus allowing our experiments to be reproduced, and to be used as baselines, by other researchers. We also describe the application of the above authorship verification system, using these datasets as training data, for investigating the authorship of two medieval epistles whose authorship has been disputed by scholars.Source: ACM JOURNAL ON COMPUTING AND CULTURAL HERITAGE, vol. 3 (issue 15)
DOI: 10.1145/3485822
Metrics:
Corbara S., Moreo Fernandez A., Sebastiani F., Tavoni M.
We present and make available MedLatinEpi and MedLatinLit, two datasets of medieval Latin texts to be used in research on computational authorship analysis. MedLatinEpi and MedLatinLit consist of 294 and 30 curated texts, respectively, labelled by author; MedLatinEpi texts are of epistolary nature, while MedLatinLit texts consist of literary comments and treatises about various subjects. As such, these two datasets lend themselves to supporting research in authorship analysis tasks, such as authorship attribution, authorship verification, or same-author verification. Along with the datasets we provide experimental results, obtained on these datasets, for the authorship verification task, i.e., the task of predicting whether a text of unknown authorship was written by a candidate author or not. We also make available the source code of the authorship verification system we have used, thus allowing our experiments to be reproduced, and to be used as baselines, by other researchers. We also describe the application of the above authorship verification system, using these datasets as training data, for investigating the authorship of two medieval epistles whose authorship has been disputed by scholars.Source: ACM JOURNAL ON COMPUTING AND CULTURAL HERITAGE, vol. 3 (issue 15)
DOI: 10.1145/3485822
Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | CNR IRIS | CNR IRIS
2022
Conference article
Open Access
Rhythmic and psycholinguistic features for authorship tasks in the Spanish parliament: evaluation and analysis
Corbara S., Chulvi B., Rosso P., Moreo Fernandez A.
Among the many tasks of the authorship field, Authorship Identification aims at uncovering the author of a document, while Author Profiling focuses on the analysis of personal characteristics of the author(s), such as gender, age, etc. Methods devised for such tasks typically focus on the style of the writing, and are expected not to make inferences grounded on the topics that certain authors tend to write about. In this paper, we present a series of experiments evaluating the use of topic-agnostic feature sets for Authorship Identification and Author Profiling tasks in Spanish political language. In particular, we propose to employ features based on rhythmic and psycholinguistic patterns, obtained via different approaches of text masking that we use to actively mask the underlying topic. We feed these feature sets to a SVM learner, and show that they lead to results that are comparable to those obtained by a BETO transformer, when the latter is trained on the original text, i.e., potentially learning from topical information. Moreover, we further investigate the results for the different authors, showing that variations in performance are partially explainable in terms of the authors' political affiliation and communication style.DOI: 10.1007/978-3-031-13643-6_6
Project(s): AI4Media
Metrics:
Corbara S., Chulvi B., Rosso P., Moreo Fernandez A.
Among the many tasks of the authorship field, Authorship Identification aims at uncovering the author of a document, while Author Profiling focuses on the analysis of personal characteristics of the author(s), such as gender, age, etc. Methods devised for such tasks typically focus on the style of the writing, and are expected not to make inferences grounded on the topics that certain authors tend to write about. In this paper, we present a series of experiments evaluating the use of topic-agnostic feature sets for Authorship Identification and Author Profiling tasks in Spanish political language. In particular, we propose to employ features based on rhythmic and psycholinguistic patterns, obtained via different approaches of text masking that we use to actively mask the underlying topic. We feed these feature sets to a SVM learner, and show that they lead to results that are comparable to those obtained by a BETO transformer, when the latter is trained on the original text, i.e., potentially learning from topical information. Moreover, we further investigate the results for the different authors, showing that variations in performance are partially explainable in terms of the authors' political affiliation and communication style.DOI: 10.1007/978-3-031-13643-6_6
Project(s): AI4Media
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2022
Other
Open Access
AIMH research activities 2022
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A. D., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability.This report summarize the 2022 activities of the research group.DOI: 10.32079/isti-ar-2022/002
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A. D., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability.This report summarize the 2022 activities of the research group.DOI: 10.32079/isti-ar-2022/002
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2021
Other
Open Access
AIMH research activities 2021
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Coccomini D., Ciampi L., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2021 activities of the research group.DOI: 10.32079/isti-ar-2021/003
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Coccomini D., Ciampi L., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2021 activities of the research group.DOI: 10.32079/isti-ar-2021/003
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2020
Other
Open Access
MedLatin1 and MedLatin2: Two Datasets for the Computational Authorship Analysis of Medieval Latin Texts
Corbara S., Moreo Fernandez A., Sebastiani F., Tavoni M.
We present and make available MedLatin1 and MedLatin2, two datasets of medieval Latin texts to be used in research on computational authorship analysis. MedLatin1 and MedLatin2 consist of 294 and 30 curated texts, respectively, labelled by author, with MedLatin1 texts being of an epistolary nature and MedLatin2 texts consisting of literary comments and treatises about various subjects. As such, these two datasets lend themselves to supporting research in authorship analysis tasks, such as authorship attribution, authorship verification, or same-author verification.
Corbara S., Moreo Fernandez A., Sebastiani F., Tavoni M.
We present and make available MedLatin1 and MedLatin2, two datasets of medieval Latin texts to be used in research on computational authorship analysis. MedLatin1 and MedLatin2 consist of 294 and 30 curated texts, respectively, labelled by author, with MedLatin1 texts being of an epistolary nature and MedLatin2 texts consisting of literary comments and treatises about various subjects. As such, these two datasets lend themselves to supporting research in authorship analysis tasks, such as authorship attribution, authorship verification, or same-author verification.
See at:
arxiv.org | CNR IRIS | ISTI Repository | CNR IRIS
2020
Other
Open Access
AIMH research activities 2020
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Carrara F., Casarosa V., Ciampi L., Concordia C., Corbara S., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Thanos C., Trupiano L., Vadicamo L., Vairo C.
Annual Report of the Artificial Intelligence for Media and Humanities laboratory (AIMH) research activities in 2020.DOI: 10.32079/isti-ar-2020/001
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Carrara F., Casarosa V., Ciampi L., Concordia C., Corbara S., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Thanos C., Trupiano L., Vadicamo L., Vairo C.
Annual Report of the Artificial Intelligence for Media and Humanities laboratory (AIMH) research activities in 2020.DOI: 10.32079/isti-ar-2020/001
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2019
Contribution to book
Open Access
The Epistle to Cangrande Through the Lens of Computational Authorship Verification
Corbara S., Moreo Fernandez A. D., Sebastiani F., Tavoni M.
The Epistle to Cangrande is one of the most controversial among the works of Italian poet Dante Alighieri. For more than a hundred years now, scholars have been debating over its real paternity, i.e., whether it should be considered a true work by Dante or a forgery by an unnamed author. In this work we address this philological problem through the methodologies of (supervised) Computational Authorship Verification, by training a classifier that predicts whether a given work is by Dante Alighieri or not. We discuss the system we have set up for this endeavour, the training set we have assembled, the experimental results we have obtained, and some issues that this work leaves open.DOI: 10.1007/978-3-030-30754-7_15
Metrics:
Corbara S., Moreo Fernandez A. D., Sebastiani F., Tavoni M.
The Epistle to Cangrande is one of the most controversial among the works of Italian poet Dante Alighieri. For more than a hundred years now, scholars have been debating over its real paternity, i.e., whether it should be considered a true work by Dante or a forgery by an unnamed author. In this work we address this philological problem through the methodologies of (supervised) Computational Authorship Verification, by training a classifier that predicts whether a given work is by Dante Alighieri or not. We discuss the system we have set up for this endeavour, the training set we have assembled, the experimental results we have obtained, and some issues that this work leaves open.DOI: 10.1007/978-3-030-30754-7_15
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | doi.org | CNR IRIS | CNR IRIS
2019
Conference article
Open Access
The Epistle to Cangrande through the Lens of computational authorship verification
Corbara S., Moreo Fernandez A., Sebastiani F., Tavoni M.
The Epistle to Cangrande is one of the most debated documents in the production of the Italian poet Dante Alighieri. For more than a hundred years scholars have been debating over its real paternity, whether it should be considered a work by Dante or a malicious forgery by an unnamed author. In this work, we try to address this philological problem through the methodologies of computational authorship verification and machine learning, by training a classifier on a dataset of medieval Latin prose texts and by using a set of authorship-related features. Although the project is still in a preliminary phase, the early results seem to confirm the hypothesis of a forgery.Source: CEUR WORKSHOP PROCEEDINGS, pp. 29-35. Milan, Italy, July 17-18, 2019
Corbara S., Moreo Fernandez A., Sebastiani F., Tavoni M.
The Epistle to Cangrande is one of the most debated documents in the production of the Italian poet Dante Alighieri. For more than a hundred years scholars have been debating over its real paternity, whether it should be considered a work by Dante or a malicious forgery by an unnamed author. In this work, we try to address this philological problem through the methodologies of computational authorship verification and machine learning, by training a classifier on a dataset of medieval Latin prose texts and by using a set of authorship-related features. Although the project is still in a preliminary phase, the early results seem to confirm the hypothesis of a forgery.Source: CEUR WORKSHOP PROCEEDINGS, pp. 29-35. Milan, Italy, July 17-18, 2019
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS