Abstract
In this paper, we propose a simple method to predict salient locations from news article text using a knowledge base (KB). The proposed method uses a dictionary of locations created from the KB to identify occurrences of locations in the text and uses the hierarchical information between entities in the KB for assigning appropriate saliency scores to regions. It allows prediction at arbitrary region units and has only a few hyperparameters that need to be tuned. We show using manually annotated news articles that the proposed method improves the f-measure by > 0.12 compared to multiple baselines.- Anthology ID:
- 2020.aacl-main.15
- Volume:
- Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Editors:
- Kam-Fai Wong, Kevin Knight, Hua Wu
- Venue:
- AACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 116–121
- Language:
- URL:
- https://aclanthology.org/2020.aacl-main.15
- DOI:
- Cite (ACL):
- Mei Sasaki, Shumpei Okura, and Shingo Ono. 2020. A Simple Text-based Relevant Location Prediction Method using Knowledge Base. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 116–121, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- A Simple Text-based Relevant Location Prediction Method using Knowledge Base (Sasaki et al., AACL 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.aacl-main.15.pdf