@inproceedings{lendvai-etal-2020-detection,
title = "Detection of Reading Absorption in User-Generated Book Reviews: Resources Creation and Evaluation",
author = "Lendvai, Piroska and
Dar{\'a}nyi, S{\'a}ndor and
Geng, Christian and
Kuijpers, Moniek and
Lopez de Lacalle, Oier and
Mensonides, Jean-Christophe and
Rebora, Simone and
Reichel, Uwe",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.595",
pages = "4835--4841",
abstract = "To detect how and when readers are experiencing engagement with a literary work, we bring together empirical literary studies and language technology via focusing on the affective state of absorption. The goal of our resource development is to enable the detection of different levels of reading absorption in millions of user-generated reviews hosted on social reading platforms. We present a corpus of social book reviews in English that we annotated with reading absorption categories. Based on these data, we performed supervised, sentence level, binary classification of the explicit presence vs. absence of the mental state of absorption. We compared the performances of classical machine learners where features comprised sentence representations obtained from a pretrained embedding model (Universal Sentence Encoder) vs. neural classifiers in which sentence embedding vector representations are adapted or fine-tuned while training for the absorption recognition task. We discuss the challenges in creating the labeled data as well as the possibilities for releasing a benchmark corpus.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>To detect how and when readers are experiencing engagement with a literary work, we bring together empirical literary studies and language technology via focusing on the affective state of absorption. The goal of our resource development is to enable the detection of different levels of reading absorption in millions of user-generated reviews hosted on social reading platforms. We present a corpus of social book reviews in English that we annotated with reading absorption categories. Based on these data, we performed supervised, sentence level, binary classification of the explicit presence vs. absence of the mental state of absorption. We compared the performances of classical machine learners where features comprised sentence representations obtained from a pretrained embedding model (Universal Sentence Encoder) vs. neural classifiers in which sentence embedding vector representations are adapted or fine-tuned while training for the absorption recognition task. We discuss the challenges in creating the labeled data as well as the possibilities for releasing a benchmark corpus.</abstract>
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<date>2020-05</date>
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%0 Conference Proceedings
%T Detection of Reading Absorption in User-Generated Book Reviews: Resources Creation and Evaluation
%A Lendvai, Piroska
%A Darányi, Sándor
%A Geng, Christian
%A Kuijpers, Moniek
%A Lopez de Lacalle, Oier
%A Mensonides, Jean-Christophe
%A Rebora, Simone
%A Reichel, Uwe
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F lendvai-etal-2020-detection
%X To detect how and when readers are experiencing engagement with a literary work, we bring together empirical literary studies and language technology via focusing on the affective state of absorption. The goal of our resource development is to enable the detection of different levels of reading absorption in millions of user-generated reviews hosted on social reading platforms. We present a corpus of social book reviews in English that we annotated with reading absorption categories. Based on these data, we performed supervised, sentence level, binary classification of the explicit presence vs. absence of the mental state of absorption. We compared the performances of classical machine learners where features comprised sentence representations obtained from a pretrained embedding model (Universal Sentence Encoder) vs. neural classifiers in which sentence embedding vector representations are adapted or fine-tuned while training for the absorption recognition task. We discuss the challenges in creating the labeled data as well as the possibilities for releasing a benchmark corpus.
%U https://aclanthology.org/2020.lrec-1.595
%P 4835-4841
Markdown (Informal)
[Detection of Reading Absorption in User-Generated Book Reviews: Resources Creation and Evaluation](https://aclanthology.org/2020.lrec-1.595) (Lendvai et al., LREC 2020)
ACL
- Piroska Lendvai, Sándor Darányi, Christian Geng, Moniek Kuijpers, Oier Lopez de Lacalle, Jean-Christophe Mensonides, Simone Rebora, and Uwe Reichel. 2020. Detection of Reading Absorption in User-Generated Book Reviews: Resources Creation and Evaluation. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4835–4841, Marseille, France. European Language Resources Association.