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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Entity Linking is one of the essential tasks of information extraction and natural language understanding.
We introduce KOSMOS, a knowledge retrieval system based on the constructed knowledge graph of social media and mainstream media documents.
A challenge for named entity disambiguation (NED), the task of mapping textual mentions to entities in a knowledge base, is how to disambiguate entities that appear rarely in the training data, termed tail entities.
In this paper, we present SoftwareKG - a knowledge graph that contains information about software mentions from more than 51, 000 scientific articles from the social sciences.
Ranked #1 on Named Entity Recognition on SoSciSoCi
Entity linking - connecting entity mentions in a natural language utterance to knowledge graph (KG) entities is a crucial step for question answering over KGs.
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.
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).
We propose a new global entity disambiguation (ED) model based on contextualized embeddings of words and entities.
Ranked #1 on Entity Disambiguation on MSNBC