Our proposed framework considers three features, including text similarity, entity popularity, and entity relationship. First we adopt LeaderRank algorithm on ...
This paper proposes a new method to collectively disambiguate mentions in documents that adopts LeaderRank algorithm on the graph model to rank entities.
Our proposed framework considers three features, including text similarity, entity popularity, and entity relationship. First we adopt LeaderRank algorithm on ...
TL;DR: This paper proposes a new method to collectively disambiguate mentions in documents that adopts LeaderRank algorithm on the graph model to rank ...
Jia et al. [54] proposed a method to disambiguate mentions in documents by combining LeaderRank with entity popularity to rank entities.
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Sep 24, 2021 · This paper proposes an entity disambiguation model EDEGE for knowledge graph fusion, which fuses the entity-relationship vector similarity and entity subgraph- ...
Missing: LeaderRank. | Show results with:LeaderRank.
The goal of entity linking is to link entity mentions in the document to their corresponding entity in a knowledge base. The prevalent approaches can be ...
Jul 5, 2024 · Their method combines a named entity recognition tool with novel similarity measures based on Linked Data to improve disambiguation accuracy.
Missing: LeaderRank. | Show results with:LeaderRank.
In this paper, we propose an entity disambiguation method based on multi-task learning for Chinese short texts with limited contextual information and informal ...
Missing: LeaderRank. | Show results with:LeaderRank.
May 13, 2024 · In this paper, we propose a novel framework, eXtreme Multi-label Ranking for Entity Disambiguation (XMRED), to address this challenge.
Missing: LeaderRank. | Show results with:LeaderRank.