Anaphora Resolution for Improving Spatial Relation Extraction from Text

Umar Manzoor, Parisa Kordjamshidi


Abstract
Spatial relation extraction from generic text is a challenging problem due to the ambiguity of the prepositions spatial meaning as well as the nesting structure of the spatial descriptions. In this work, we highlight the difficulties that the anaphora can make in the extraction of spatial relations. We use external multi-modal (here visual) resources to find the most probable candidates for resolving the anaphoras that refer to the landmarks of the spatial relations. We then use global inference to decide jointly on resolving the anaphora and extraction of the spatial relations. Our preliminary results show that resolving anaphora improves the state-of-the-art results on spatial relation extraction.
Anthology ID:
W18-1407
Volume:
Proceedings of the First International Workshop on Spatial Language Understanding
Month:
June
Year:
2018
Address:
New Orleans
Editors:
Parisa Kordjamshidi, Archna Bhatia, James Pustejovsky, Marie-Francine Moens
Venue:
SpLU
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
53–62
Language:
URL:
https://aclanthology.org/W18-1407
DOI:
10.18653/v1/W18-1407
Bibkey:
Cite (ACL):
Umar Manzoor and Parisa Kordjamshidi. 2018. Anaphora Resolution for Improving Spatial Relation Extraction from Text. In Proceedings of the First International Workshop on Spatial Language Understanding, pages 53–62, New Orleans. Association for Computational Linguistics.
Cite (Informal):
Anaphora Resolution for Improving Spatial Relation Extraction from Text (Manzoor & Kordjamshidi, SpLU 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/W18-1407.pdf
Data
Visual Genome