Abstract
In this paper, we consider the named entity linking (NEL) problem. We assume a set of queries, named entities, that have to be identified within a knowledge base. This knowledge base is represented by a text database paired with a semantic graph, endowed with a classification of entities (ontology). We present state-of-the-art methods in NEL, and propose a new method for individual identification requiring few annotated data samples. We demonstrate its scalability and performance over standard datasets, for several ontology configurations. Our approach is well-motivated for integration in real systems. Indeed, recent deep learning methods, despite their capacity to improve experimental precision, require lots of parameter tuning along with large volume of annotated data.- Anthology ID:
- D19-5303
- Volume:
- Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 17–25
- Language:
- URL:
- https://aclanthology.org/D19-5303
- DOI:
- 10.18653/v1/D19-5303
- Cite (ACL):
- Sammy Khalife and Michalis Vazirgiannis. 2019. Scalable graph-based method for individual named entity identification. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 17–25, Hong Kong. Association for Computational Linguistics.
- Cite (Informal):
- Scalable graph-based method for individual named entity identification (Khalife & Vazirgiannis, TextGraphs 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D19-5303.pdf