Group	Function name	Description
Neural NER models	BTC	Model trained on the Broad Twitter Corpus
BTC+c	Model trained on the Broad Twitter Corpus + postprocessing
SEC	Model trained on SEC-filings
SEC+c	Model trained on SEC-filings + postprocessing
conll2003	Model trained on CoNLL 2003
conll2003+c	Model trained on CoNLL 2003 + postprocessing
core_web.md	Model trained on Ontonotes 5.0
core_web.md+c	Model trained on Ontonotes 5.0 + postprocessing
Gazetteers	wiki.cased	Gazetteer (case-sensitive) using Wikipedia entries
multitoken.wikis.cased	Same as above, but restricted to multitoken entities
wiki Uncased	Gazetteer (case-insensitive) using Wikipedia entries
multitoken.wikis Uncased	Same as above, but restricted to multitoken entities
wiki.Small.cased	Gazetteer (case-sensitive) using Wikipedia entries with non-empty description
multitoken.wikis.Small.cased	Same as above, but restricted to multitoken entities
wiki.Small Uncased	Gazetteer (case-insensitive) using Wikipedia entries with non-empty description
multitoken.wikis.Small.cased	Same as above, but restricted to multitoken entities
company.cased	Gazetteer (case-sensitive) using a large list of company names
multitoken.company.cased	Same as above, but restricted to multitoken entities
company Uncased	Gazetteer from a large list of company names (case-insensitive)
multitoken.company Uncased	Same as above, but restricted to multitoken entities
crunchbase.cased	Gazetteer (case-sensitive) using the Crunchbase Open Data Map
multitoken.crunchbase.cased	Same as above, but restricted to multitoken entities
crunchbase Uncased	Gazetteer (case-insensitive) using the Crunchbase Open Data Map
multitoken.crunchbase Uncased	Same as above, but restricted to multitoken entities
geo.cased	Gazetteer (case-sensitive) using the Geonames database
multitoken.geo.cased	Same as above, but restricted to multitoken entities
geo Uncased	Gazetteer (case-insensitive) using the Geonames database
multitoken.geo Uncased	Same as above, but restricted to multitoken entities
product.cased	Gazetteer (case-sensitive) using products extracted from DBPedia
multitoken.product.cased	Same as above, but restricted to multitoken entities
product Uncased	Gazetteer (case-insensitive) using products extracted from DBPedia
multitoken.product Uncased	Same as above, but restricted to multitoken entities
Heuristic functions	date_detector	Detection of entities of type DATE
time_detector	Detection of entities of type TIME
money_detector	Detection of entities of type MONEY
number_detector	Detection of entities CARDINAL, ORDINAL, PERCENT and QUANTITY
legal_detector	Detection of entities of type LAW
misc_detector	Detection of entities of type NORP, LANGUAGE, FAC or EVENT
full_name_detector	Heuristic function to detect full person names
company_type_detector	Detection of companies with a legal type suffix
nnp_detector	Detection of sequences of tokens with NNp as POS-tag
infrequent_nnp_detector	Detection of sequences of tokens with NNp as POS-tag
proper_detector	+ including at least one infrequent token (rank > 15000 in vocabulary)
infrequentproper_detector	Detection of proper names based on casing
proper2_detector	Detection of proper names based on casing
infrequentproper2_detector	Detection of proper names based on casing + at least one infrequent token
compound_detector	Detection of proper noun phrases with compound dependency relations
infrequentcompound_detector	Detection of proper noun phrases with compound dependency relations
snips	+ including at least one infrequent token
snips	Probabilistic parser specialised in the recognition of dates, times, money amounts, percents, and cardinal/ordinal values
Doc-level functions	doc_history	Entity classification based on already introduced entities in the document
docmajority.cased	Entity classification based on majority labels in document (case-sensitive)
docmajority Uncased	Entity classification based on majority labels in document (case-insensitive)
Table 3: Full list of labelling functions employed in the experiments. The neural NER models are provided in two versions: one that directly outputs the raw model predictions, and one that runs a shallow postprocessing step on the model predictions to correct known recognition errors (for instance, ensuring that a numeric amount that is either preceded or followed by a currency symbol is always classified as an entity of type MONEY).