Tagging Location Phrases in Text

Paul McNamee, James Mayfield, Cash Costello, Caitlyn Bishop, Shelby Anderson


Abstract
For over thirty years researchers have studied the problem of automatically detecting named entities in written language. Throughout this time the majority of such work has focused on detection and classification of entities into coarse-grained types like: PERSON, ORGANIZATION, and LOCATION. Less attention has been focused on non-named mentions of entities, including non-named location phrases such as “the medical clinic in Telonge” or “2 km below the Dolin Maniche bridge”. In this work we describe the Location Phrase Detection task to identify such spans. Our key accomplishments include: developing a sequential tagging approach; crafting annotation guidelines; building annotated datasets for English and Russian news; and, conducting experiments in automated detection of location phrases with both statistical and neural taggers. This work is motivated by extracting rich location information to support situational awareness during humanitarian crises such as natural disasters.
Anthology ID:
2020.lrec-1.557
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4521–4528
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.557
DOI:
Bibkey:
Cite (ACL):
Paul McNamee, James Mayfield, Cash Costello, Caitlyn Bishop, and Shelby Anderson. 2020. Tagging Location Phrases in Text. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4521–4528, Marseille, France. European Language Resources Association.
Cite (Informal):
Tagging Location Phrases in Text (McNamee et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.557.pdf