  Identifying named entities such as a person, location or organization, in
documents can highlight key information to readers. Training Named Entity
Recognition (NER) models requires an annotated data set, which can be a
time-consuming labour-intensive task. Nevertheless, there are publicly
available NER data sets for general English. Recently there has been interest
in developing NER for legal text. However, prior work and experimental results
reported here indicate that there is a significant degradation in performance
when NER methods trained on a general English data set are applied to legal
text. We describe a publicly available legal NER data set, called E-NER, based
on legal company filings available from the US Securities and Exchange
Commission's EDGAR data set. Training a number of different NER algorithms on
the general English CoNLL-2003 corpus but testing on our test collection
confirmed significant degradations in accuracy, as measured by the F1-score, of
between 29.4\% and 60.4\%, compared to training and testing on the E-NER
collection.
