A Multi-task Approach to Predict Likability of Books

Suraj Maharjan, John Arevalo, Manuel Montes, Fabio A. González, Thamar Solorio


Abstract
We investigate the value of feature engineering and neural network models for predicting successful writing. Similar to previous work, we treat this as a binary classification task and explore new strategies to automatically learn representations from book contents. We evaluate our feature set on two different corpora created from Project Gutenberg books. The first presents a novel approach for generating the gold standard labels for the task and the other is based on prior research. Using a combination of hand-crafted and recurrent neural network learned representations in a dual learning setting, we obtain the best performance of 73.50% weighted F1-score.
Anthology ID:
E17-1114
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1217–1227
Language:
URL:
https://www.aclweb.org/anthology/E17-1114
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/E17-1114.pdf