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The Entity Disambiguation and Linking (EDL) task matches entity mentions intext to a unique Knowledge Base (KB) identifier such as a Wikipedia or Freebaseid. It plays a critical role in the construction of a high quality informationnetwork, and can be further leveraged for a variety of information retrievaland NLP tasks such as text categorization and document tagging. EDL is acomplex and challenging problem due to ambiguity of the mentions and real worldtext being multi-lingual. Moreover, EDL systems need to have high throughputand should be lightweight in order to scale to large datasets and run onoff-the-shelf machines. More importantly, these systems need to be able toextract and disambiguate dense annotations from the data in order to enable anInformation Retrieval or Extraction task running on the data to be moreefficient and accurate. In order to address all these challenges, we presentthe Lithium EDL system and algorithm - a high-throughput, lightweight,language-agnostic EDL system that extracts and correctly disambiguates 75% moreentities than state-of-the-art EDL systems and is significantly faster thanthem.

High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data