Entity Disambiguation
57 papers with code • 11 benchmarks • 12 datasets
Entity Disambiguation is the task of linking mentions of ambiguous entities to their referent entities in a knowledge base such as Wikipedia.
Source: Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation
Benchmarks
These leaderboards are used to track progress in Entity Disambiguation
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Datasets
CoNLL
AIDA CoNLL-YAGO
AQUAINT
TAC 2010
Wikidata-Disamb
Latest papers with no code
Joint Entity Linking with Deep Reinforcement Learning
Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base.
Discovering Entities with Just a Little Help from You
For such entities, mining a suitable representation in a fully automated fashion is very difficult, resulting in poor linking accuracy.
Harnessing Historical Corrections to build Test Collections for Named Entity Disambiguation
One collection focuses on the properties of defects and one on the evaluation of disambiguation algorithms.
Story Disambiguation: Tracking Evolving News Stories across News and Social Streams
This is particularly the case for local news stories that are easily over shadowed by other trending stories, and for complex news stories with ambiguous content in noisy stream environments.
Pangloss: Fast Entity Linking in Noisy Text Environments
Entity linking is the task of mapping potentially ambiguous terms in text to their constituent entities in a knowledge base like Wikipedia.
Multimodal Named Entity Disambiguation for Noisy Social Media Posts
We introduce the new Multimodal Named Entity Disambiguation (MNED) task for multimodal social media posts such as Snapchat or Instagram captions, which are composed of short captions with accompanying images.
diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora
Named Entity Disambiguation (NED) systems perform well on news articles and other texts covering a specific time interval.
A Sequence Learning Method for Domain-Specific Entity Linking
Recent collective Entity Linking studies usually promote global coherence of all the mapped entities in the same document by using semantic embeddings and graph-based approaches.
Joint Neural Entity Disambiguation with Output Space Search
In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS).
Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All
For the first time, we show that the semantic relationships between the mentioned entities are in fact less dense than expected.
