Named Entity Linking with Entity Representation by Multiple Embeddings

Autor:	Vasilyev, Oleg, Dauenhauer, Alex, Dharnidharka, Vedant, Bohannon, John
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	We propose a simple and practical method for named entity linking (NEL), based on entity representation by multiple embeddings. To explore this method, and to review its dependency on parameters, we measure its performance on Namesakes, a highly challenging dataset of ambiguously named entities. Our observations suggest that the minimal number of mentions required to create a knowledge base (KB) entity is very important for NEL performance. The number of embeddings is less important and can be kept small, within as few as 10 or less. We show that our representations of KB entities can be adjusted using only KB data, and the adjustment can improve NEL performance. We also compare NEL performance of embeddings obtained from tuning language model on diverse news texts as opposed to tuning on more uniform texts from public datasets XSum, CNN / Daily Mail. We found that tuning on diverse news provides better embeddings. Comment: 12 pages, 14 figures, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.10498 Zobrazit plný text záznamu View this record from Arxiv