license:cc-by-4.0 region:us
# Introduction

The GermEval 2014 NER Shared Task is an event that makes available CC-licensed German data with NER annotation with the 
goal of significantly advancing the state of the art in German NER and to push the field of NER towards nested 
representations of named entities.

The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following 
properties:  The data was sampled from German Wikipedia and News Corpora as a collection of citations. The dataset 
covers over 31,000 sentences corresponding to over 590,000 tokens. The NER annotation uses the NoSta-D guidelines, 
which extend the Tübingen Treebank guidelines, using four main NER categories with sub-structure, and annotating 
embeddings among NEs such as [ORG FC Kickers [LOC Darmstadt]].

# Dataset

## Labels

### Fine-grained labels indicating NER subtypes

German morphology is comparatively productive (at least when compared to 
English). There is a considerable amount of word formation through both overt (non-zero) derivation and compounding, in 
particular for nouns. This gives rise to morphologically complex words that are not identical to, but stand in a direct 
relation to, Named Entities. The Shared Task corpus treats these as NE instances but marks them as special subtypes by 
introducing two fine-grained labels: -deriv marks derivations from NEs such as the previously mentioned englisch 
(“English”), and -part marks compounds including a NE as a subsequence deutschlandweit (“Germany-wide”).

### Embedded markables

Almost all extant corpora with Named Entity annotation assume that NE annotation is “flat”, that is, each word in the 
text can form part of at most one NE chunk. Clearly, this is an oversimplification. Consider the noun phase Technische 
Universitat Darmstadt ¨ (“Technical University (of) Darmstadt”). It denotes an organization (label ORG), but also holds 
another NE, Darmstadt, which is a location (label LOC). To account for such cases, the Shared Task corpus is annotated 
with two levels of Named Entities. It captures at least one level of smaller NEs being embedded in larger NEs.

## Statistics

The data used for the GermEval 2014 NER Shared Task builds on the dataset annotated by (Benikova et al., 2014).
In 
this dataset, sentences taken from German Wikipedia articles and online news were used as a collection of citations, 
then annotated according to extended NoSta-D guidelines and eventually distributed under the CC-BY license. As already 
described above, those guidelines use four main categories with sub-structure and nesting.

The dataset is distributed contains overall more than 31,000 sentences with over 590,000 tokens. Those were divided in 
the following way: the training set consists of 24,000 sentences, the development set of 2,200 sentences and the test 
set of 5,100 sentences. The test set labels were not available to the participants until after the deadline. The 
distribution of the categories over the whole dataset is shown in Table 1. Care was taken to ensure the even dispersion 
of the categories in the subsets. The entire dataset contains over 41,000 NEs, about 7.8% of them embedded in other NEs 
(nested NEs), about 11.8% are derivations (deriv) and about 5.6% are parts of NEs concatenated with other words (part).

## Format

The tab-separated format used in this dataset is similar to the CoNLL-Format. As illustrated in Table 2, the format 
used in the dataset additionally contains token numbers per sentence in the first column and a comment line indicating 
source and data before each sentence. The second column contains the tokens. The third column encodes the outer NE 
spans, the fourth column the inner ones. The BIO-scheme was used in order to encode the NE spans. In our challenge, 
further nested columns were not considered.

## Summary

In summary, we distinguish between 12 classes of NEs: four main classes PERson, LOCation, ORGanisation, and OTHer and 
their subclasses, annotated at two levels (“inner” and “outer” chunks). The challenge of this setup is that while it 
technically still allows a simple classification approach it introduces a recursive structure that calls for the 
application of more general machine learning or other automatically classifying methods that go beyond plain sequence 
tagging.
@inproceedings{benikova14:_germev_named_entit_recog_shared_task,
  added-at = {2017-04-03T19:29:52.000+0000},
  address = {Hildesheim, Germany},
  author = {Benikova, Darina and Biemann, Chris and Kisselew, Max and Pad\'o, Sebastian},
  biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2132d938a7afe8639e78156fb9d756b20/sp},
  booktitle = {Proceedings of the KONVENS GermEval workshop},
  interhash = {6cad5d4fdd6a07dbefad4221ba7d8d44},
  intrahash = {132d938a7afe8639e78156fb9d756b20},
  keywords = {myown workshop},
  pages = {104--112},
  timestamp = {2017-04-03T17:30:49.000+0000},
  title = {{GermEval 2014 Named Entity Recognition Shared Task: Companion Paper}},
  year = 2014
}
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license: cc-by-4.0
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          '2': I-LOC
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          '13': B-OTH
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          '16': I-OTHderiv
          '17': B-OTHpart
          '18': I-OTHpart
          '19': B-PER
          '20': I-PER
          '21': B-PERderiv
          '22': I-PERderiv
          '23': B-PERpart
          '24': I-PERpart
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