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Dataset Card for NorNE: Norwegian Named Entities
Dataset Summary
NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons,organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.
There are 3 main configs in this dataset each with 3 versions of the NER tag set. When accessing the bokmaal
, nynorsk
, or combined
configs the NER tag set will be comprised of 9 tags: GPE_ORG
, GPE_LOC
, ORG
, LOC
, PER
, PROD
, EVT
, DRV
, and MISC
. The two special types GPE_LOC
and GPE_ORG
can easily be altered depending on the task, choosing either the more general GPE
tag or the more specific LOC
/ORG
tags, conflating them with the other annotations of the same type. To access these reduced versions of the dataset, you can use the configs bokmaal-7
, nynorsk-7
, combined-7
for the NER tag set with 7 tags ( ORG
, LOC
, PER
, PROD
, EVT
, DRV
, MISC
), and bokmaal-8
, nynorsk-8
, combined-8
for the NER tag set with 8 tags (LOC_
and ORG_
: ORG
, LOC
, GPE
, PER
, PROD
, EVT
, DRV
, MISC
). By default, the full set (9 tags) will be used. See Annotations for further details.
Supported Tasks and Leaderboards
NorNE ads named entity annotations on top of the Norwegian Dependency Treebank.
Languages
Both Norwegian Bokmål (bokmaal
) and Nynorsk (nynorsk
) are supported as different configs in this dataset. An extra config for the combined languages is also included (combined
). See the Annotation section for details on accessing reduced tag sets for the NER feature.
Dataset Structure
Each entry contains text sentences, their language, identifiers, tokens, lemmas, and corresponding NER and POS tag lists.
Data Instances
An example of the train
split of the bokmaal
config.
{'idx': '000001',
'lang': 'bokmaal',
'lemmas': ['lam', 'og', 'piggvar', 'på', 'bryllupsmeny'],
'ner_tags': [0, 0, 0, 0, 0],
'pos_tags': [0, 9, 0, 5, 0],
'text': 'Lam og piggvar på bryllupsmenyen',
'tokens': ['Lam', 'og', 'piggvar', 'på', 'bryllupsmenyen']}
Data Fields
Each entry is annotated with the next fields:
idx
(int
), text (sentence) identifier from the NorNE datasetlang
(str
), language variety, eitherbokmaal
,nynorsk
orcombined
text
(str
), plain texttokens
(List[str]
), list of tokens extracted fromtext
lemmas
(List[str]
), list of lemmas extracted fromtokens
ner_tags
(List[int]
), list of numeric NER tags for each token intokens
pos_tags
(List[int]
), list of numeric PoS tags for each token intokens
An example DataFrame obtained from the dataset:
idx | lang | text | tokens | lemmas | ner_tags | pos_tags | |
---|---|---|---|---|---|---|---|
0 | 000001 | bokmaal | Lam og piggvar på bryllupsmenyen | [Lam, og, piggvar, på, bryllupsmenyen] | [lam, og, piggvar, på, bryllupsmeny] | [0, 0, 0, 0, 0] | [0, 9, 0, 5, 0] |
1 | 000002 | bokmaal | Kamskjell, piggvar og lammefilet sto på menyen... | [Kamskjell, ,, piggvar, og, lammefilet, sto, p... | [kamskjell, $,, piggvar, og, lammefilet, stå, ... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | [0, 1, 0, 9, 0, 15, 2, 0, 2, 8, 6, 0, 1] |
2 | 000003 | bokmaal | Og til dessert: Parfait à la Mette-Marit. | [Og, til, dessert, :, Parfait, à, la, Mette-Ma... | [og, til, dessert, $:, Parfait, à, la, Mette-M... | [0, 0, 0, 0, 7, 8, 8, 8, 0] | [9, 2, 0, 1, 10, 12, 12, 10, 1] |
Data Splits
There are three splits: train
, validation
and test
.
Config | Split | Total |
---|---|---|
bokmaal |
train |
15696 |
bokmaal |
validation |
2410 |
bokmaal |
test |
1939 |
nynorsk |
train |
14174 |
nynorsk |
validation |
1890 |
nynorsk |
test |
1511 |
combined |
test |
29870 |
combined |
validation |
4300 |
combined |
test |
3450 |
Dataset Creation
Curation Rationale
- A name in this context is close to Saul Kripke's definition of a name, in that a name has a unique reference and its meaning is constant (there are exceptions in the annotations, e.g. "Regjeringen" (en. "Government")).
- It is the usage of a name that determines the entity type, not the default/literal sense of the name,
- If there is an ambiguity in the type/sense of a name, then the the default/literal sense of the name is chosen (following Markert and Nissim, 2002).
For more details, see the "Annotation Guidelines.pdf" distributed with the corpus.
Source Data
Data was collected using blogs and newspapers in Norwegian, as well as parliament speeches and governamental reports.
Initial Data Collection and Normalization
The texts in the Norwegian Dependency Treebank (NDT) are manually annotated with morphological features, syntactic functions and hierarchical structure. The formalism used for the syntactic annotation is dependency grammar.
The treebanks consists of two parts, one part in Norwegian Bokmål (nob
) and one part in Norwegian Nynorsk (nno
).
Both parts contain around 300.000 tokens, and are a mix of different non-fictional genres.
See the NDT webpage for more details.
Annotations
The following types of entities are annotated:
- Person (
PER
): Real or fictional characters and animals - Organization (
ORG
): Any collection of people, such as firms, institutions, organizations, music groups, sports teams, unions, political parties etc. - Location (
LOC
): Geographical places, buildings and facilities - Geo-political entity (
GPE
): Geographical regions defined by political and/or social groups. A GPE entity subsumes and does not distinguish between a nation, its region, its government, or its people - Product (
PROD
): Artificially produced entities are regarded products. This may include more abstract entities, such as speeches, radio shows, programming languages, contracts, laws and ideas. - Event (
EVT
): Festivals, cultural events, sports events, weather phenomena, wars, etc. Events are bounded in time and space. - Derived (
DRV
): Words (and phrases?) that are dervied from a name, but not a name in themselves. They typically contain a full name and are capitalized, but are not proper nouns. Examples (fictive) are "Brann-treneren" ("the Brann coach") or "Oslo-mannen" ("the man from Oslo"). - Miscellaneous (
MISC
): Names that do not belong in the other categories. Examples are animals species and names of medical conditions. Entities that are manufactured or produced are of type Products, whereas thing naturally or spontaneously occurring are of type Miscellaneous.
Furthermore, all GPE
entities are additionally sub-categorized as being either ORG
or LOC
, with the two annotation levels separated by an underscore:
GPE_LOC
: Geo-political entity, with a locative sense (e.g. "John lives in Spain")GPE_ORG
: Geo-political entity, with an organisation sense (e.g. "Spain declined to meet with Belgium")
The two special types GPE_LOC
and GPE_ORG
can easily be altered depending on the task, choosing either the more general GPE
tag or the more specific LOC
/ORG
tags, conflating them with the other annotations of the same type. This means that the following sets of entity types can be derived:
- 7 types, deleting
_GPE
:ORG
,LOC
,PER
,PROD
,EVT
,DRV
,MISC
- 8 types, deleting
LOC_
andORG_
:ORG
,LOC
,GPE
,PER
,PROD
,EVT
,DRV
,MISC
- 9 types, keeping all types:
ORG
,LOC
,GPE_LOC
,GPE_ORG
,PER
,PROD
,EVT
,DRV
,MISC
The class distribution is as follows, broken down across the data splits of the UD version of NDT, and sorted by total counts (i.e. the number of examples, not tokens within the spans of the annotatons):
Type | Train | Dev | Test | Total |
---|---|---|---|---|
PER |
4033 | 607 | 560 | 5200 |
ORG |
2828 | 400 | 283 | 3511 |
GPE_LOC |
2132 | 258 | 257 | 2647 |
PROD |
671 | 162 | 71 | 904 |
LOC |
613 | 109 | 103 | 825 |
GPE_ORG |
388 | 55 | 50 | 493 |
DRV |
519 | 77 | 48 | 644 |
EVT |
131 | 9 | 5 | 145 |
MISC |
8 | 0 | 0 | 0 |
To access these reduced versions of the dataset, you can use the configs bokmaal-7
, nynorsk-7
, combined-7
for the NER tag set with 7 tags ( ORG
, LOC
, PER
, PROD
, EVT
, DRV
, MISC
), and bokmaal-8
, nynorsk-8
, combined-8
for the NER tag set with 8 tags (LOC_
and ORG_
: ORG
, LOC
, GPE
, PER
, PROD
, EVT
, DRV
, MISC
). By default, the full set (9 tags) will be used.
Additional Information
Dataset Curators
NorNE was created as a collaboration between Schibsted Media Group, Språkbanken at the National Library of Norway and the Language Technology Group at the University of Oslo.
NorNE was added to 🤗 Datasets by the AI-Lab at the National Library of Norway.
Licensing Information
The NorNE corpus is published under the same license as the Norwegian Dependency Treebank
Citation Information
This dataset is described in the paper NorNE: Annotating Named Entities for Norwegian by Fredrik Jørgensen, Tobias Aasmoe, Anne-Stine Ruud Husevåg, Lilja Øvrelid, and Erik Velldal, accepted for LREC 2020 and available as pre-print here: https://arxiv.org/abs/1911.12146.
@inproceedings{johansen2019ner,
title={NorNE: Annotating Named Entities for Norwegian},
author={Fredrik Jørgensen, Tobias Aasmoe, Anne-Stine Ruud Husevåg,
Lilja Øvrelid, and Erik Velldal},
booktitle={LREC 2020},
year={2020},
url={https://arxiv.org/abs/1911.12146}
}
Contributions
Thanks to @versae for adding this dataset.
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