Abstract
The task of Named Entity Linking is to link entity mentions in the document to their correct entries in a knowledge base and to cluster NIL mentions. Ambiguous, misspelled, and incomplete entity mention names are the main challenges in the linking process. We propose a novel approach that combines two state-of-the-art models ― for entity disambiguation and for paraphrase detection ― to overcome these challenges. We consider name variations as paraphrases of the same entity mention and adopt a paraphrase model for this task. Our approach utilizes a graph-based disambiguation model based on Personalized Page Rank, and then refines and clusters its output using the paraphrase similarity between entity mention strings. It achieves a competitive performance of 80.5% in B3+F clustering score on diagnostic TAC EDL 2014 data.- Anthology ID:
- L16-1088
- Volume:
- Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
- Month:
- May
- Year:
- 2016
- Address:
- Portorož, Slovenia
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 556–560
- Language:
- URL:
- https://aclanthology.org/L16-1088
- DOI:
- Cite (ACL):
- Maria Pershina, Yifan He, and Ralph Grishman. 2016. Entity Linking with a Paraphrase Flavor. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 556–560, Portorož, Slovenia. European Language Resources Association (ELRA).
- Cite (Informal):
- Entity Linking with a Paraphrase Flavor (Pershina et al., LREC 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/L16-1088.pdf
- Code
- masha-p/paraphrase_flavor
- Data
- PIT