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- W2256406302 abstract "With unstructured text on the web and social media increasing at a furious pace, it is all the more important to develop techniques that can ease semantic understanding of text data for humans. One of the key tasks in this process is that of entity linking; identifying mentions of entities in text. Consider the line that reads The Prime Minister came under harsh criticism over the Immigration Act Without any additional context, it is not obvious to humans as to who is being talked about. An entity linking technique that has the entity database at its disposal, however, can easily figure out that the mention Prime Minister refers to the Prime Minister of UK since the mention of Immigration Act 2014 in the same sentence narrows down the search space from the set of all countries that have Prime Ministers to just UK. Such linking of text documents to entities enables easier understanding for the reader, as well as improved accuracy in automated tasks such as text document clustering, classification and information retrieval." @default.
- W2256406302 created "2016-06-24" @default.
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- W2256406302 date "2014-12-17" @default.
- W2256406302 modified "2023-09-23" @default.
- W2256406302 title "Entity linking: detecting entities within text" @default.
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