This paper addresses the identification of all contributors of an intellectual work, when they are recorded in bibliographic data but in unstructured form. National bibliographies are very reliable on representing the first author of a work, but frequently, secondary contributors are represented in the statements of responsibility that are transcribed by the cataloguer from the book into the bibliographic records. The identification of work contributors mentioned in statements of responsibility is a typical motivation for the application of information extraction techniques. This paper presents an approach developed for the specific application scenario of the ARROW rights infrastructure being deployed in several European countries to assist in the determination of the copyright status of works that may not be under public domain. Our approach performed reliably in most languages and bibliographic datasets of at least one million records, achieving precision and recall above 0.97 on five of the six evaluated datasets. We conclude that the approach can be reliably applied to other national bibliographies and languages.

1. Word Occurrence Based Extraction
of Work Contributors from
Statements of Responsibility
Nuno Freire
The European Library
TPDL-2013
Valletta, September 2013

2. Overview
Statements of responsibility from library bibliographic data:
“French Canadian freely arranged by Katherine K. Davis”.
“ed. by Peter Noever ; with a forew. by Frank O. Gehry; and contrib. by
Coop Himmelblau.”
“W. Lange, A.C. Zeven and N.G. Hogenboom, editors”
“Vicente Aleixandre ; estudio previo, selección y notas de Leopoldo de
Luis”
Extracting work contributors for use in a rights
infrastructure: ARROW
http://arrow-net.eu

3. Outline
 The context
• The ARROW rights infrastructure
• The use of national bibliographies in ARROW




The problem
The approach
Evaluation
Conclusion and future work

4. The ARROW rights
infrastructure
 ARROW aims to support mass digitisation projects
with automated ways to clear the rights of the books to
be digitised.
 To identify and clear the rights associated with a book
a complex process needs to be undertaken:
•
•
•
•
•
Determine the work(s) contained within the book
Identify all the other expressions of the same work(s)
Identify the publisher(s) and contributor(s) involved
Determine the dates of publication at work level
Determine whether that work(s), and not the book itself, is
still in commerce
• If necessary, obtain any licenses from the rights holders or
collective rights organizations
4

5. What is ARROW
 A rights infrastructure and system for the
identification of:
• Rights status
• In or out of copyright
• In or out of print / commercialised or not
• Rights
• Which rights are involved
• Right holders
• Authors
• Publishers
• How and where to clear the rights
• Orphan Works and their registration
5

6. Sources of Information in ARROW
 ARROW makes information available
from several sources:
• The European Library:
• National bibliographies - to identify the book and to
cluster it with all other books containing the same
intellectual work
• Virtual International Authority File - to better identify
the authors and support the identification of in copyright
works
• Books in Print database - to know if any of the books
concerned are actively commercialised by any publisher
• Reproduction Rights Organisation – to see if they know
or can trace the rightholders
6

7. The ARROW
Workflow

8. The Role of Libraries
The Role of Libraries
••NationalLibraries as Metadata Providers
National Libraries as Metadata Providers
••
Provide the National Bibliographies to The
Provide the National Bibliographies to The
European Library
European Library

9. The Role of The European Library (TEL)
The Role of The European Library (TEL)
•To match library requests with national bibliographies
•To match library requests with national bibliographies
••Identifyall other manifestations that potentially share
Identify all other manifestations that potentially share
intellectual work with a manifestation
intellectual work with a manifestation
••Tocreate a Work record: work metadata, manifestations,
To create a Work record: work metadata, manifestations,
contributors, etc.
contributors, etc.

10. The Role of Books-in-Print (BIP)
The Role of Books-in-Print (BIP)
••Toprovide data about in print/out of print status
To provide data about in print/out of print status
••Toprovide data about publishers
To provide data about publishers
•To add new manifestation records of the work
•To add new manifestation records of the work

11. The Role of Reproduction Rights Organisation (RRO)
The Role of Reproduction Rights Organisation (RRO)
•RROs as Metadata Provider
•RROs as Metadata Provider
••
To provide data about authors and publishers
To provide data about authors and publishers
••
To provide data about available licenses
To provide data about available licenses
…
…

12. Statements of responsibility
 These statements usually contain information
about authorship, editors, photographers,
translators, and others involved in creating the
work
 In printed books, the statement of responsibility
is typically present on the title page
• The statement of responsibility is transcribed by the cataloguer
exactly as it appears in the book
(according to Anglo-American Cataloguing Rules)

13. Examples of statements of
responsibility
“French Canadian freely arranged by Katherine K. Davis”.
“ed. by Peter Noever ; with a forew. by Frank O. Gehry; and contrib. by
Coop Himmelblau.”
“W. Lange, A.C. Zeven and N.G. Hogenboom, editors”
“by Pamela and Neal Priestland”
“Vicente Aleixandre ; estudio previo, selección y notas de Leopoldo de
Luis”

14. The problem
 National bibliographies are reliable on
representing in structured form the first author of
a work
 But secondary contributors are often not
represented in structured form
 Secondary contributors may reside only within
the statements of responsibility

15. The approach
 To approach the problem as a Named Entity Recognition
task in text that may not be grammatically correct, thus
lacking lexical evidence
 Some requirements from the ARROW context
• Easily applicable to several languages
• The outcomes of the recognition task must be explainable
 Design decisions
• Exploring the structured data within national bibliographies
• By analysis of the frequency of word occurrences in names of
persons, and in other textual data
• Using word occurrence frequency allows to
• bypass the need for building training sets
• be able to provide simpler explanations of the name recognition
results

16. The process – pre-processing
 A pre-processing of each national
bibliography is performed:
• Word frequency is calculated
• The frequency values are normalized, for
independence on the size of the national bibliography
• The pre-processing results in four dictionaries:
•
•
•
•
Words in titles
Words in person’s surnames
Words in other parts of person’s names, than the surname
Words that appear in lowercase in person names
(such as “von” in German names, or “de” in Portuguese
names)
• The dictionaries contain the normalized frequency
associated the words

17. The process – bibliographic record
processing
 The named entity recognition is performed for a
record as follows:
• Statement of responsibility is tokenized
• The person names are recognized by comparing the
tokens with the dictionaries
• The recognized names are compared against the
names of the contributors present in the structured
fields of the record.
• If no similar name exists in the record, the contributor
is added to the record in a structured data field

18. The process – named entity
recognition
Possible token sequences used to locate person names:
(in Augmented Backus–Naur Form)
non-ambiguous-surname
/
(
initial /
non-ambiguous-first-name /
non-ambiguous-surname /
non-ambiguous-non-capitalized-name
)
*(initial / first-name / surname / non-capitalized-name)
surname
(more details on the definition of these tokens are included in the paper)

19. Evaluation data set
(size of bibliographies and evaluation samples)
National Bibliography
British Library
German National
Library
National Library of the
Netherlands
National Library of
Greece
Central Institute for the
Union Catalogue of
Italian Libraries
Royal Library of
Belgium
Total
records
Main
language
Evaluation sample
Statements of
responsibility
Referred
Persons
13.4 million
English
205
328
9.4 million
German
200
378
3.2 million
Dutch
200
335
0.4 million
Greek
297
379
12.4 million
Italian
224
297
203
387
1329
2104
1 million
French and
Dutch
Total:

20. Evaluation results
Exact match
metric
Dataset
Partial match
metric
Precision
British Library
German National Library
National Library of the
Netherlands
National Library of
Greece
Central Institute for the
Union Catalogue of
Italian Libraries
Royal Library of Belgium
Overall:
Recall
Precision
Recall
0.981
0.975
0.979
0.934
0.991
0.992
0.991
0.992
0.973
0.875
0.977
0.979
0.656
0.414
0.758
0.868
0.97
0.896
0.971
0.973
0.981
0.948
0.959
0.837
0.981
0.958
0.982
0.963

21. Evaluation results analysis
 The main causes of recognition errors:
• Foreign person names negatively affected recall
• Names of persons used in names of
organizations negatively affected precision
• Two persons with same surname mentioned
together negatively affected recall. As for
example:
• “hrsg. von Volker und Michael Kriegeskorte”
• “by Pamela and Neal Priestland”

22. Conclusions
 The approach performed reliably in most
languages and bibliographic datasets
• Datasets of at least one million records
• Precision and recall above 0.97 on all but one dataset
 The results obtained on the Greek national
bibliography were not satisfactory
• This dataset has distinct characteristics from the
others:
• smaller size,
• a different alphabet
• different language
• Further investigation of the Greek national
bibliography is necessary

23. Future work
 Evaluation of the impact of this solution on the
final results of the rights clearance process of
ARROW
 Building the dictionaries from comprehensive
source of names of persons
• Virtual International Authority File (VIAF)
• International Standard Name Identifier (ISNI)
 Further functionality:
• recognition of organization names
• recognition of the role of the recognized contributors
(illustrator, editor, etc.)
 Other application scenarios
• Functional Requirements for Bibliographic Records
• Resource Description and Access

24. Acknowledgments
 The European Library
• Marcela Strelcova, Chiara Latronico and
Eva Kralt-Yap
 Associazione Italiana Editori
 University of Innsbruck
 This work was partially supported by the
ARROWplus project, with co-funding by the
European Commission programme
eContentplus
Co-funded by the
Community
programme
eContentplus

25. T hank you
Questions or comments?
Contact:
Nuno Freire – nuno.freire@kb.nl