Paper Info
Title
Discovering and disambiguating named entities in text
Abstract
Disambiguating named entities in natural language texts maps ambiguous names to canonical entities registered in a knowledge base such as DBpedia, Freebase, or YAGO. Knowing the specific entity is an important asset for several other tasks, e.g. entity-based information retrieval or higher-level information extraction. Our approach to named entity disambiguation makes use of several ingredients: the prior probability of an entity being mentioned, the similarity between the context of the mention in the text and an entity, as well as the coherence among the entities. Extending this method, we present a novel and highly efficient measure to compute the semantic coherence between entities. This measure is especially powerful for long-tail entities or such entities that are not yet present in the knowledge base. Reliably identifying names in the input text that are not part of the knowledge base is the current focus of our work.
Year
DOI
Venue
2013
10.1145/2483574.2483582
SIGMOD/PODS PhD Symposium
Keywords
Field
DocType
semantic coherence,input text,knowledge base,ambiguous name,long-tail entity,entity-based information retrieval,efficient measure,specific entity,entity disambiguation,higher-level information extraction,entity linking,semantic search,semantic relatedness
Data mining,Computer science,Weak entity,Natural language processing,Artificial intelligence,SGML entity,Knowledge base,Entity linking,Semantic similarity,Information retrieval,Information extraction,Natural language,Prior probability
Conference
Citations 
PageRank 
References 
1
0.36
13
Authors
1
Name
Order
Citations
PageRank
Johannes Hoffart1136252.62