TY - GEN
T1 - Figure retrieval from collections of research articles
AU - Kuzi, Saar
AU - Zhai, Cheng Xiang
N1 - Funding Information:
Acknowledgments. We thank the reviewers for their useful comments. This material is based upon work supported by the National Science Foundation under Grant No. 1801652.
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - In this paper, we introduce and study a new task of figure retrieval in which the retrieval units are figures of research articles and the task is to rank figures with response to a query. As a first step toward addressing this task, we focus on textual queries and represent a figure using text extracted from its article. We suggest and study the effectiveness of several retrieval methods for the task. We build a test collection by using research articles from the ACL Anthology corpus and treating figure captions as queries. While having some limitations, using this data set we were able to obtain some interesting preliminary results on the relative effectiveness of different representations of a figure and different retrieval methods, which also shed some light regarding possible types of information need, and potential challenges in figure retrieval.
AB - In this paper, we introduce and study a new task of figure retrieval in which the retrieval units are figures of research articles and the task is to rank figures with response to a query. As a first step toward addressing this task, we focus on textual queries and represent a figure using text extracted from its article. We suggest and study the effectiveness of several retrieval methods for the task. We build a test collection by using research articles from the ACL Anthology corpus and treating figure captions as queries. While having some limitations, using this data set we were able to obtain some interesting preliminary results on the relative effectiveness of different representations of a figure and different retrieval methods, which also shed some light regarding possible types of information need, and potential challenges in figure retrieval.
UR - http://www.scopus.com/inward/record.url?scp=85064857280&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064857280&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-15712-8_45
DO - 10.1007/978-3-030-15712-8_45
M3 - Conference contribution
AN - SCOPUS:85064857280
SN - 9783030157111
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 696
EP - 710
BT - Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings
A2 - Azzopardi, Leif
A2 - Stein, Benno
A2 - Hauff, Claudia
A2 - Mayr, Philipp
A2 - Hiemstra, Djoerd
A2 - Fuhr, Norbert
PB - Springer
T2 - 41st European Conference on Information Retrieval, ECIR 2019
Y2 - 14 April 2019 through 18 April 2019
ER -