21. A system for coreference resolution comprising: memory which stores a set of document clusters, each cluster in the set comprising a set of text documents; an entity extractor which is configured for identifying instances of each of a set of candidate named entities in the document clusters, the instances of a respective candidate named entity being text elements; a socio-temporal features extractor which is configured for computing at least one socio-temporal feature for each of a set of pairs of the candidate named entities, based on event profiles for the candidate named entities in each pair, the event profiles each comprising an optionally normalized vector of size k, where k is the number of document clusters in the set, in which each index of the vector is based on the occurrences of the identified instances of the candidate named entity in a respective one of the k clusters; a prediction component which is configured for outputting a similarity score for each pair of the candidate named entities based on the at least one socio-temporal feature; a merging component which is configured for merging the candidate named entities of each pair into a common real named entity, when the computed similarity score meets a predetermined threshold; and a processor which implements at least one of the socio-temporal features extractor and the prediction component.
21. A system for coreference resolution comprising: memory which stores a set of document clusters, each cluster in the set comprising a set of text documents; an entity extractor which is configured for identifying instances of each of a set of candidate named entities in the document clusters, the instances of a respective candidate named entity being text elements; a socio-temporal features extractor which is configured for computing at least one socio-temporal feature for each of a set of pairs of the candidate named entities, based on event profiles for the candidate named entities in each pair, the event profiles each comprising an optionally normalized vector of size k, where k is the number of document clusters in the set, in which each index of the vector is based on the occurrences of the identified instances of the candidate named entity in a respective one of the k clusters; a prediction component which is configured for outputting a similarity score for each pair of the candidate named entities based on the at least one socio-temporal feature; a merging component which is configured for merging the candidate named entities of each pair into a common real named entity, when the computed similarity score meets a predetermined threshold; and a processor which implements at least one of the socio-temporal features extractor and the prediction component. 22. The system of claim 21 , wherein the prediction component is configured for inputting the at least one socio-temporal feature to a classifier which has been trained to output a similarity score for a pair of candidate named entities based on the socio-temporal feature.