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
Is Aligning Embedding Spaces a Challenging Task? A Study on Heterogeneous Embedding Alignment Methods
Representation Learning of words and Knowledge Graphs (KG) into low dimensional vector spaces along with its applications to many real-world scenarios have recently gained momentum.
Improving Entity Linking by Modeling Latent Entity Type Information
Existing state of the art neural entity linking models employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to assess topic level context compatibility.
LATTE: Latent Type Modeling for Biomedical Entity Linking
This is of significant importance in the biomedical domain, where it could be used to semantically annotate a large volume of clinical records and biomedical literature, to standardized concepts described in an ontology such as Unified Medical Language System (UMLS).
Contextualized End-to-End Neural Entity Linking
We propose yet another entity linking model (YELM) which links words to entities instead of spans.
Representing text as abstract images enables image classifiers to also simultaneously classify text
We introduce a novel method for converting text data into abstract image representations, which allows image-based processing techniques (e. g. image classification networks) to be applied to text-based comparison problems.
Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation
We present a new local entity disambiguation system.
Wikipedia as a Resource for Text Analysis and Retrieval
This tutorial examines the role of Wikipedia in tasks related to text analysis and retrieval.
Neural Relation Extraction for Knowledge Base Enrichment
This way, NED errors may cause extraction errors that affect the overall precision and recall. To address this problem, we propose an end-to-end relation extraction model for KB enrichment based on a neural encoder-decoder model.
Improving Neural Entity Disambiguation with Graph Embeddings
Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base.
Improving Knowledge Base Construction from Robust Infobox Extraction
One important approach to constructing a comprehensive knowledge base is to extract information from Wikipedia infobox tables to populate an existing KB.
