Statistical named entity recognisers require costly hand-labelled training data and, as a result, most existing corpora are small. We exploit Wikipedia to create a massive corpus of named entity annotated text. We transform Wikipedia's links into named entity annotations by classifying the target articles into common entity types (e.g. person, organisation and location). Comparing to MUC, CONLL and BBN corpora, Wikipedia generally performs better than other cross-corpus train/test pairs.
The system introduced in REF transforms links to Wikipedia articles into named entity annotations by classifying the target articles into the classic named entity types Person, Location, and Organization.
Transforming Wikipedia into Named Entity Training Data
ALTA
Computer Science