The Labeled Segmentation of Printed Books

Lara McConnaughey, Jennifer Dai, David Bamman


Abstract
We introduce the task of book structure labeling: segmenting and assigning a fixed category (such as Table of Contents, Preface, Index) to the document structure of printed books. We manually annotate the page-level structural categories for a large dataset totaling 294,816 pages in 1,055 books evenly sampled from 1750-1922, and present empirical results comparing the performance of several classes of models. The best-performing model, a bidirectional LSTM with rich features, achieves an overall accuracy of 95.8 and a class-balanced macro F-score of 71.4.
Anthology ID:
D17-1077
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
737–747
Language:
URL:
https://www.aclweb.org/anthology/D17-1077
DOI:
10.18653/v1/D17-1077
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1077.pdf