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2 results about "Entity linking" patented technology

In natural language processing, entity linking, also referred to as named entity linking (NEL), named entity disambiguation (NED), named entity recognition and disambiguation (NERD) or named entity normalization (NEN) is the task of assigning a unique identity to entities (such as famous individuals, locations, or companies) mentioned in text. For example, given the sentence "Paris is the capital of France", the idea is to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred to as "Paris". Entity linking is different from named entity recognition (NER) in that NER identifies the occurrence of a named entity in text but it does not identify which specific entity it is (see Differences from other techniques).

CN-DBpedia-based entity identification and linking system and method

ActiveCN108491375AExtended vocabulary spaceReasonable calculationSemantic analysisSpecial data processing applicationsEntity linkingLearning based
The present invention discloses a CN-DBpedia-based entity identification and linking system and method. The system comprises an entity linking module and an entity identification module; the entity linking module comprises a synonym matching unit and an entity linking unit; and the entity identification module comprises a tokenizer, a word probability calculation unit, and an entity discriminatingunit. According to the technical scheme of the present invention, a semantic relationship between an entity and a word is constructed, so that the relationship with the entity can be mined in a few of context; a machine learning-based entity recognition algorithm is combined with an unsupervised word segmentation algorithm, the rationality of entity name division is considered from the perspective of globality, the vocabulary space of word segmentation is expanded, and the word formation probability of entity words can be calculated by using a more reasonable algorithm; and with a linking first and then identification manner, the semantic information of the text is fully utilized in the entity identification, and better word segmentation and entity identification are realized.
Owner:FUDAN UNIV
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