1.
NER for Europeana Newspapers
Clemens Neudecker (@cneudecker)
Staatsbibliothek zu Berlin –
Preußischer Kulturbesitz

2.
Background

3.
Why Named Entity Recognition?
• Analysis* of query log files from the National Library of Wales
newspaper website: a vast majority of searches queries contain
either person or place names
* Paul Gooding, Exploring Usage of Digital Newspaper Archives through Web Log Analysis:
A Case Study of Welsh Newspapers Online, presented at DH2014, Lausanne)
• Improving Information
Retrieval
• Linking to authority files
(Linked Data)
• Historical Social Network
Analysis (HNA/SNA)

4.
Languages
• Dutch (1614 – 1900)
• French (1814 – 1944)
• German (1721 – 1949)
• Together approx. 50% of the total collection

5.
Many challenges
• Historical data (language)
• Noisy data (OCR)
• Multilingual data
• Lack of extensive metadata
• Lack of open resources
(tagged corpora, gazetteers)
• Lack of common annotation guidelines
• Limitations of annotation tools

6.
Technology

7.
Reuse of existing NER tools
• Simple evaluation of
– Apache OpenNLP
– Stanford CoreNLP
– GATE
• Choice of using Stanford CoreNLP since
– Java-based (thread safe, scalable)
– Good performance (f-measure)
– Strong and active community
– Rather robust against noisy input (CRF)

8.
Approach
• Adaptation of Stanford CoreNLP by the
KB National Library of the Netherlands
to directly consume ENMAP (= Europeana
Newspapers METS/ALTO profile) objects

9.
Approach
• Export option ALTO v3 with tags added
<String STYLEREFS="ID7" HEIGHT="132.0" WIDTH="570.0" HPOS="5937.0"
VPOS="3279.0" CONTENT="Reynolds" WC="0.95238096" TAGREFS="Tag5">
</String>
<String STYLEREFS="ID7" HEIGHT="102.0" WIDTH="540.0" HPOS="18438.0"
VPOS="22008.0" CONTENT="Baltimore" WC="0.82539684" TAGREFS="Tag10">
</String>
…
<Tags>
<NamedEntityTag ID="Tag5" TYPE="Person" LABEL="Reynolds"/>
<NamedEntityTag ID="Tag10" TYPE=”Location" LABEL=”Baltimore"/>
</Tags>

10.
Annotation
• Quick evaluation of annotation tools:
– BRAT
– WebANNO
– INL Attestation Tool
• Choice of INL Attestation Tool since:
– Optimized for tagging speed
– Supported by consortium partner (INL/IVDNT)

11.
Corpus creation
• Selection of 100 pages each per language
• Processing of the OCRed texts with
StanfordNER to get initial tagging results
• Manual verification and annotation

12.
Corpus statistics
Language # tokens # PER # LOC # ORG
French 207,000 5,672 5,614 2,574
Dutch 182,483 4,492 4,448 1,160
German 96,735 7,914 6,143 2,784
Language # tokens # PER # LOC # ORG
French 100% 2,75% 2,71% 1,24%
Dutch 100% 2,46% 2,44% 0,64%
German 100% 8,18% 6,35% 2,88%
Language Word-Error-Rate (Bag of Words) Reading Order Success Rate
French 16,6% 19,9%
Dutch 17,6% 23,2%
German 15,9% / 21,9% 13,6%

13.
ner-app
https://github.com/EuropeanaNewspapers/ner-app

14.
ner-corpora
https://github.com/EuropeanaNewspapers/ner-corpora

15.
Evaluation: NL

16.
Evaluation FR

17.
Evaluation DE
• A Named Entity Recognition Shootout for
German
M. Riedl and S. Padó. Proceedings of ACL,
Melbourne, Australia, (2018).To appear.

18.
NER vs OCR success rate
0.25
0.35
0.45
0.55
0.65
0.75
0.85
0.95
NER
OCR

19.
Future Plans

20.
Improving performance
• Possible additional features
– Distributional similarity (Clark 2003)
– Semantic generalization (Faruqui & Padò 2010)
– Word embeddings (Braune 2017)
• Gazetteers
– Person names, historical place names
• Data cleanup and improvement
– https://github.com/EuropeanaNewspapers/
ner-corpora/wiki

21.
Trias NER
• Combination and voting of different NER
classifiers, e.g.
– Stanford CoreNLP
– Spacy
– NLTK
• Inspiration:
https://github.com/KBNLresearch/Trias_NER

22.
Disambiguation
• Disambiguation of person and place names
• Inspiration:
https://github.com/KBNLresearch/europeana
np-dbpedia-disambiguation

23.
Linking
• Linking of recognised and disambiguated NE‘s
to authority files (e.g. Wikidata, GND)
• Inspiration:
https://github.com/KBNLresearch/dac