Abstract
This paper demonstrates the importance of relation equivalence for entity translation pair discovery. Existing approach of understanding relation equivalence has focused on using explicit features of cooccurring entities. In this paper, we explore latent features of temporality for understanding relation equivalence, and empirically show that the explicit and latent features complement each other. Our proposed hybrid approach of using both explicit and latent features improves relation translation by 0.16 F1-score, and in turn improves entity translation by 0.02.
| Original language | English |
|---|---|
| Title of host publication | Long Papers |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 848-853 |
| Number of pages | 6 |
| ISBN (Print) | 9781937284732 |
| DOIs | |
| Publication status | Published - 2014 Jan 1 |
| Event | 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Baltimore, MD, United States Duration: 2014 Jun 22 → 2014 Jun 27 |
Publication series
| Name | 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference |
|---|---|
| Volume | 2 |
Other
| Other | 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 |
|---|---|
| Country | United States |
| City | Baltimore, MD |
| Period | 14/6/22 → 14/6/27 |
All Science Journal Classification (ASJC) codes
- Language and Linguistics
- Linguistics and Language
Fingerprint Dive into the research topics of 'Understanding relation temporality of entities'. Together they form a unique fingerprint.
Cite this
Lee, T., & Hwang, S. W. (2014). Understanding relation temporality of entities. In Long Papers (pp. 848-853). (52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference; Vol. 2). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/p14-2137