Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models

08/12/2020
by   Isaiah Onando Mulang', et al.
17

Pretrained Transformer models have emerged as state-of-the-art approaches that learn contextual information from text to improve the performance of several NLP tasks. These models, albeit powerful, still require specialized knowledge in specific scenarios. In this paper, we argue that context derived from a knowledge graph (in our case: Wikidata) provides enough signals to inform pretrained transformer models and improve their performance for named entity disambiguation (NED) on Wikidata KG. We further hypothesize that our proposed KG context can be standardized for Wikipedia, and we evaluate the impact of KG context on state-of-the-art NED model for the Wikipedia knowledge base. Our empirical results validate that the proposed KG context can be generalized (for Wikipedia), and providing KG context in transformer architectures considerably outperforms the existing baselines, including the vanilla transformer models.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/25/2021

CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata

In this paper, we propose CHOLAN, a modular approach to target end-to-en...
research
05/10/2023

PAI at SemEval-2023 Task 2: A Universal System for Named Entity Recognition with External Entity Information

The MultiCoNER II task aims to detect complex, ambiguous, and fine-grain...
research
05/13/2022

ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation

We present ViT5, a pretrained Transformer-based encoder-decoder model fo...
research
03/15/2016

Evaluating the word-expert approach for Named-Entity Disambiguation

Named Entity Disambiguation (NED) is the task of linking a named-entity ...
research
12/16/2022

How to disagree well: Investigating the dispute tactics used on Wikipedia

Disagreements are frequently studied from the perspective of either dete...
research
04/15/2020

Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph

In this paper, we describe an embedding-based entity recommendation fram...
research
01/30/2023

GE-Blender: Graph-Based Knowledge Enhancement for Blender

Although the great success of open-domain dialogue generation, unseen en...

Please sign up or login with your details

Forgot password? Click here to reset