Entity Disambiguation
56 papers with code • 10 benchmarks • 11 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
Datasets
Most implemented papers
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking
We demonstrate the accuracy of our approach on a wide range of benchmark datasets, showing that it matches, and in many cases outperforms, existing state-of-the-art methods.
Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words.
Entity Linking with a Paraphrase Flavor
The task of Named Entity Linking is to link entity mentions in the document to their correct entries in a knowledge base and to cluster NIL mentions.
Learning Distributed Representations of Texts and Entities from Knowledge Base
Given a text in the KB, we train our proposed model to predict entities that are relevant to the text.
Named Entity Disambiguation for Noisy Text
We address the task of Named Entity Disambiguation (NED) for noisy text.
DeepType: Multilingual Entity Linking by Neural Type System Evolution
The wealth of structured (e. g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence.
Collective Entity Disambiguation with Structured Gradient Tree Boosting
To the best of our knowledge, our work is the first one that employs the structured gradient tree boosting (SGTB) algorithm for collective entity disambiguation.
Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories
We use the Wikidata knowledge base and available question answering datasets to create benchmarks for entity linking on question answering data.
ELDEN: Improved Entity Linking Using Densified Knowledge Graphs
Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG).