I’ll leave this link here as additional reference. This passage mentions something important about the entity extraction aspect of lookup tables.
Regular expressions and lookup tables are adding additional features to ner_crf which mark whether a word was matched by a regular expression or lookup table entry. As it is one feature of many, the component ner_crf can still ignore an entity although it was matched, however in general ner_crf develops a bias for these features. Note that this can also stop the conditional random field from generalizing: if all entity examples in your training data are matched by a regular expression, the conditional random field will learn to focus on the regular expression feature and ignore the other features. If you then have a message with a certain entity which is not matched by the regular expression, ner_crf will probably not be able to detect it. Especially the use of lookup tables makes ner_crf prone for overfitting.