Entity Disambiguation is the task of linking mentions of ambiguous entities to their referent entities in a knowledge base such as Wikipedia.
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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.
Ranked #2 on Entity Disambiguation on TAC2010
We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations.
Ranked #2 on Entity Disambiguation on WNED-CWEB
Hyperlinks and other relations in Wikipedia are a extraordinary resource which is still not fully understood.
Given a text in the KB, we train our proposed model to predict entities that are relevant to the text.
Ranked #1 on Entity Linking on AIDA-CoNLL (Accuracy metric)
We investigate entity linking in the context of a question answering task and present a jointly optimized neural architecture for entity mention detection and entity disambiguation that models the surrounding context on different levels of granularity.
Ranked #1 on Entity Linking on WebQSP-WD