Datasets and Models for Authorship Attribution on Italian Personal Writings

Gaetana Ruggiero; Albert Gatt; Malvina Nissim

Datasets and Models for Authorship Attribution on Italian Personal Writings

Gaetana Ruggiero, Albert Gatt, Malvina Nissim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

Existing research on Authorship Attribution (AA) focuses on texts for which a lot of data is available (e.g novels), mainly in English. We approach AA via Authorship Verification on short Italian texts in two novel datasets, and analyze the interaction between genre, topic, gender and length. Results show that AV is feasible even with little data, but more evidence helps. Gender and topic can be indicative clues, and if not controlled for, they might overtake more specific aspects of personal style.

Original language	English
Title of host publication	Proceedings of the Seventh Italian Conference on Computational Linguistics, CLiC-it 2020, Bologna, Italy, March 1-3, 2021
Editors	Johanna Monti, Felice Dell'Orletta, Fabio Tamburini
Publisher	CEUR-WS.org
Number of pages	7
Volume	2769
Publication status	Published - 2020
Event	Italian Conference on Computational Linguistics 2020 - Bologna, Italy Duration: 1-Mar-2021 → 3-Mar-2021

Conference

Conference	Italian Conference on Computational Linguistics 2020
Abbreviated title	CLiC-it 2020
Country	Italy
City	Bologna
Period	01/03/2021 → 03/03/2021

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Datasets and Models for Authorship Attribution on Italian Personal WritingsFinal publisher's version, 252 KBLicence: CC BY

Cite this

@inproceedings{985d9f54da3e442081fa9beea995b1bc,

title = "Datasets and Models for Authorship Attribution on Italian Personal Writings",

abstract = "Existing research on Authorship Attribution (AA) focuses on texts for which a lot of data is available (e.g novels), mainly in English. We approach AA via Authorship Verification on short Italian texts in two novel datasets, and analyze the interaction between genre, topic, gender and length. Results show that AV is feasible even with little data, but more evidence helps. Gender and topic can be indicative clues, and if not controlled for, they might overtake more specific aspects of personal style.",

author = "Gaetana Ruggiero and Albert Gatt and Malvina Nissim",

year = "2020",

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editor = "Johanna Monti and Felice Dell'Orletta and Fabio Tamburini",

booktitle = "Proceedings of the Seventh Italian Conference on Computational Linguistics, CLiC-it 2020, Bologna, Italy, March 1-3, 2021",

publisher = "CEUR-WS.org",

note = "Italian Conference on Computational Linguistics 2020, CLiC-it 2020 ; Conference date: 01-03-2021 Through 03-03-2021",

}

Ruggiero, G, Gatt, A & Nissim, M 2020, Datasets and Models for Authorship Attribution on Italian Personal Writings. in J Monti, F Dell'Orletta & F Tamburini (eds), Proceedings of the Seventh Italian Conference on Computational Linguistics, CLiC-it 2020, Bologna, Italy, March 1-3, 2021. vol. 2769, CEUR-WS.org, Italian Conference on Computational Linguistics 2020, Bologna, Italy, 01/03/2021.

Datasets and Models for Authorship Attribution on Italian Personal Writings. / Ruggiero, Gaetana; Gatt, Albert; Nissim, Malvina.

Proceedings of the Seventh Italian Conference on Computational Linguistics, CLiC-it 2020, Bologna, Italy, March 1-3, 2021. ed. / Johanna Monti; Felice Dell'Orletta; Fabio Tamburini. Vol. 2769 CEUR-WS.org, 2020.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Datasets and Models for Authorship Attribution on Italian Personal Writings

AU - Ruggiero, Gaetana

AU - Gatt, Albert

AU - Nissim, Malvina

PY - 2020

Y1 - 2020

N2 - Existing research on Authorship Attribution (AA) focuses on texts for which a lot of data is available (e.g novels), mainly in English. We approach AA via Authorship Verification on short Italian texts in two novel datasets, and analyze the interaction between genre, topic, gender and length. Results show that AV is feasible even with little data, but more evidence helps. Gender and topic can be indicative clues, and if not controlled for, they might overtake more specific aspects of personal style.

AB - Existing research on Authorship Attribution (AA) focuses on texts for which a lot of data is available (e.g novels), mainly in English. We approach AA via Authorship Verification on short Italian texts in two novel datasets, and analyze the interaction between genre, topic, gender and length. Results show that AV is feasible even with little data, but more evidence helps. Gender and topic can be indicative clues, and if not controlled for, they might overtake more specific aspects of personal style.

M3 - Conference contribution

VL - 2769

BT - Proceedings of the Seventh Italian Conference on Computational Linguistics, CLiC-it 2020, Bologna, Italy, March 1-3, 2021

A2 - Monti, Johanna

A2 - Dell'Orletta, Felice

A2 - Tamburini, Fabio

PB - CEUR-WS.org

T2 - Italian Conference on Computational Linguistics 2020

Y2 - 1 March 2021 through 3 March 2021

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