Re-ranking Spotlight results with Stanford NER Sample Clauses

Re-ranking Spotlight results with Stanford NER. Since DBPedia Spotlight provides a ranked list of candidates, we can think of two possible uses of NER in combination with Spotlight:  Filter out Spotlight candidates that are not a NE, to improve precision of detection  Re-rank Spotlight candidates according to the category of NE (person/organisation/location). Only useful to help with cross-categorial ambiguities, but not intra-categorial. In the following example, we are looking at the U.S. entity: The U.S. followed with a 24.6 percent share.  Stanford NER annotates it as a LOCATION.  DBPedia Spotlight provides this ranked list of candidate annotations, with their probabilities. The good one is in second place, quite far from the first candidate: United_States_dollar (0.9837532875379671) By using the combined information (i.e. XXX has identified it as a LOCATION), we are able to boost the good candidate to the first place, i.e. United_States. The re-ranking heuristics is simple: the entity type provided by Spotlight (dbtype= PLACE) and the one provided by Stanford (nertype=LOCATION) of the top ranked candidate are compared. If they are compatible (e.g. place and location) re-rank does not happen; otherwise we look for the next candidate successively. If none matches, the highest ranked one with an empty dbtype is chosen. If the entity has not been identified by Stanford, the highest ranked is kept (i.e. no filtering takes place). Note that, due to incompleteness of the xxxxxx Xxxxxxxxx, dbtype may be empty.
Sample 1
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