Talk at IEEE Humanities Big Data Workshop 2015

1. Metaphor Mining in Historical German Novels:
An Unsupervised Learning Approach.
Stefan Pernes
University of Würzburg

2. ‣Intro
‣Dataset
‣Method
‣Examples
‣Conclusion
Overview

3. ‣Intro
‣Dataset
‣Method
‣Examples
‣Conclusion
Overview

4. “Manifestations of metaphor are frequent in
language, appearing on average in every third
sentence of general-domain text, according to
corpus studies”
- Shutova 2015
Context

5. Linguistic Metaphor
A systematic mapping, or rather, a projection of
one domain of experience (the source, e.g. war)
onto another (the target, e.g. argument)
Definition

6. Conceptual Metaphor
A cognitive 'bracket' that encompasses any
number of specific realizations (e.g. She shot down
all of my arguments)
Definition

7. Applications
‣Cognitive anthropology
‣Critical Discourse Analysis
‣Literary Studies
‣Philosophy
‣Psycholinguistics
‣…

8. ‣Intro
‣Dataset
‣Method
‣Examples
‣Conclusion
Overview

9. Corpus characteristics
‣about 1700 historical German novels
‣ranging from the early 16th to the early 20th century
‣based on: http://www.germanistik.uni-
wuerzburg.de/lehrstuehle/computerphilologie/
forschung/projekte/digibib
‣consists of 120.000 literary works, covering
almost all canonical titles
‣also includes encyclopaedias, dictionaries

10. Dev Corpus #1
143 novels / 15 mio tokens

11. Dev Corpus #2
383 novels / 41 mio tokens / from 1780

12. ‣Intro
‣Dataset
‣Method
‣Examples
‣Conclusion
Overview

13. “Selectional preferences are the tendency for a
word to semantically select or constrain which
other words may appear in a direct syntactic
relation with it.” - Roberts and Egg 2014
Selectional preferences

14. Preprocessing
‣POS-Tagging (treetagger)
‣Dependency Parsing (mate-tools)
‣using a modular NLP pipeline designed for book-
length documents
‣http://github.com/DARIAH-DE/DARIAH-DKPro-
Wrapper

15. Preprocessing
‣extracting most frequent nouns and
corresponding verbs in specific
grammatical relations: subject, direct
object, indirect object
‣in German: subject, accusative object,
dative object

16. Preprocessing
‣vector representation of nouns and
normalization
‣using Jensen-Shannon divergence to
construct noun-noun similarity matrix

17. Clustering: Connectivity based
(baseline)
Dev Corpus #1 Dev Corpus #2

18. Clustering: Subspace based
Dev Corpus #1 Dev Corpus #2

19. ‣Intro
‣Dataset
‣Method
‣Examples
‣Conclusion
Overview

20. Examples
based on:
‣Dev Corpora #1 and #2
‣Ward linkage @ 400 clusters
‣Complete linkage @ 1000 clusters

21. IDEAS ARE FOOD1
education
bildung (10): geben-dobj-0.181818181818 beanspruchen-dobj-0.0909090909091 taxieren-
dobj-0.0909090909091 voraneilen-subj-0.0909090909091 überstrahlt-dobj-0.0909090909091
ausspräch-subj-0.0909090909091 nahestehen-subj-0.0909090909091 heraustreiben-
dobj-0.0909090909091 ermangelnd-dobj-0.0909090909091 abschöpfen-dobj-0.0909090909091
memory
erinnerung (48): geben-dobj-0.135593220339 wachzurufen-dobj-0.0338983050847 stören-
dobj-0.0338983050847 mahnen-dobj-0.0338983050847 aufgrischen-dobj-0.0338983050847
verlöschen-subj-0.0169491525424 wiederzuerwecken-dobj-0.0169491525424 neubeleben-
dobj-0.0169491525424 frischen-dobj-0.0169491525424 hervorschießen-subj-0.0169491525424
hunger
hunger (10): geben-dobj-0.181818181818 erweren-dobj-0.0909090909091 büssen-
dobj-0.0909090909091 schaben-subj-0.0909090909091 überhen-iobj-0.0909090909091
verschmachten-dobj-0.0909090909091 stärkern-dobj-0.0909090909091 bittern-
subj-0.0909090909091 hinausgetreiben-subj-0.0909090909091 trainieren-iobj-0.0909090909091
1 cf. Lakoff et al. - Master Metaphor List

22. WELL-BEING IS WEALTH
money
geld (265): bekommen-dobj-0.216306156406 ausgeben-dobj-0.108153078203 leihen-
dobj-0.0549084858569 reichen-subj-0.0249584026622 aufbringen-dobj-0.0166389351082
einbringen-dobj-0.0149750415973 herausgeben-dobj-0.0133111480865 liehen-
dobj-0.0116472545757 zurückzahlen-dobj-0.0116472545757 vertun-dobj-0.00998336106489
security
sicherheit (12): bekommen-dobj-0.1875 bedroht-dobj-0.1875 fehlen-dobj-0.0625 glorios-
dobj-0.0625 erquickt-subj-0.0625 bedrücken-iobj-0.0625 mistrauen-iobj-0.0625 geringhätzen-
dobj-0.0625 geringschätzen-dobj-0.0625 sen-dobj-0.0625 betrage-dobj-0.0625 halber-
dobj-0.0625

23. LUSTFUL PERSON IS AN ANIMAL
creature
kreatur (11): haben-subj-0.388888888889 anpreisen-dobj-0.111111111111 entfremden-
subj-0.0555555555556 entquillen-dobj-0.0555555555556 herfürbringen-dobj-0.0555555555556
verübeln-dobj-0.0555555555556 fleuchen-dobj-0.0555555555556 spieen-subj-0.0555555555556
hinwegwerfen-iobj-0.0555555555556 posaunt-subj-0.0555555555556
lust
lust (48): haben-subj-0.37962962963 hättest-dobj-0.0925925925926 anwandeln-
dobj-0.0555555555556 verspüren-subj-0.0277777777778 wegzulaufen-dobj-0.0185185185185
vergieng-dobj-0.0185185185185 hest-dobj-0.0185185185185 verspürt-dobj-0.0185185185185
fabulieren-dobj-0.00925925925926 verlör-subj-0.00925925925926

24. COMMUNICATION IS TRANSFER
+ PSYCHOLOGICAL FORCES ARE PHYSICAL FORCES
scent
geruch (5): verbreiten-dobj-0.555555555556 hielten-dobj-0.111111111111 geben-
dobj-0.111111111111 naschnüffeln-dobj-0.111111111111 entgegentrügen-iobj-0.111111111111
distrust
mißtrauen (3): verbreiten-dobj-0.333333333333 umwölkt-subj-0.333333333333 aufkeimen-
subj-0.333333333333

25. EMOTIONS ARE PLANTS 2
flower
blume (47): pflücken-dobj-0.290697674419 liegen-subj-0.093023255814 lieben-
dobj-0.0581395348837 begießen-dobj-0.0348837209302 welken-dobj-0.0232558139535 duften-
dobj-0.0232558139535 duftet-subj-0.0116279069767 durchhauten-subj-0.0116279069767
hingesenken-subj-0.0116279069767 erblüht-dobj-0.0116279069767
emotion
gefühl (90): liegen-subj-0.0825688073394 ersticken-dobj-0.0642201834862 abstumpfen-
dobj-0.0275229357798 hervorraufen-dobj-0.0183486238532 halten-iobj-0.0183486238532
entspinnen-dobj-0.0183486238532 hinausdehnen-dobj-0.00917431192661 aufwekken-
dobj-0.00917431192661 anhielen-subj-0.00917431192661 arten-subj-0.00917431192661
2 not in Master Metaphor List

26. COMMUNICATION IS A LIQUID
milk
milch (11): fließen-subj-0.375 abmaß-dobj-0.0625 beschnuppern-dobj-0.0625 auflecken-
dobj-0.0625 fleussen-dobj-0.0625 vertrocknen-dobj-0.0625 abrahmen-dobj-0.0625
herniederfleussen-dobj-0.0625 austranken-dobj-0.0625 hinuntergelassen-subj-0.0625
speech
rede (50): verschlagen-dobj-0.0535714285714 fließen-subj-0.0357142857143 verschlug-
subj-0.0357142857143 abgewinnen-dobj-0.0357142857143 bestürzen-subj-0.0357142857143
hageln-dobj-0.0178571428571 kämen-dobj-0.0178571428571 heimzahlen-
subj-0.0178571428571 verwunderen-dobj-0.0178571428571 coupieren-subj-0.0178571428571

27. PHYSICAL FORCES ARE ANIMALS
bear
bär (8): nehmen-dobj-0.272727272727 erliegen-dobj-0.181818181818 läg'-
subj-0.0909090909091 herauszotteln-dobj-0.0909090909091 beschnüffeln-
subj-0.0909090909091 losrennen-subj-0.0909090909091 erlegst-dobj-0.0909090909091 plumpt-
iobj-0.0909090909091
flood
flut (14): nehmen-dobj-0.277777777778 spie-dobj-0.0555555555556 tosten-
dobj-0.0555555555556 hinauswälzen-dobj-0.0555555555556 zubrüllen-dobj-0.0555555555556
wühlen-iobj-0.0555555555556 vorüberrausch-dobj-0.0555555555556 vertropfen-
dobj-0.0555555555556 vorüberwälzen-dobj-0.0555555555556 herauffahren-
dobj-0.0555555555556

28. CAUSES ARE OBJECTS
shirt
hemd (12): anziehen-dobj-0.391304347826 anlegen-dobj-0.0869565217391 durchstechen-
dobj-0.0869565217391 herumzerren-dobj-0.0869565217391 entzweigerissen-
dobj-0.0434782608696 übergewerfen-dobj-0.0434782608696 herunterschälen-
dobj-0.0434782608696 zurückgeschlagen-dobj-0.0434782608696 strammen-
dobj-0.0434782608696 anziehst-dobj-0.0434782608696
coercion
zwang (7): anlegen-dobj-0.588235294118 überheben-dobj-0.117647058824 überziehen-
subj-0.0588235294118 befreyet-dobj-0.0588235294118 dummen-subj-0.0588235294118 yfüern-
iobj-0.0588235294118 rechn-subj-0.0588235294118

29. ‣Intro
‣Dataset
‣Method
‣Examples
‣Conclusion
Overview

30. Conclusion
(as of yet)
‣initial concept graph is robust in various settings
‣substantially more data is needed, including
normalization and balancing
‣Shutova and Sun’s (2013) original Hierarchical
Graph Factorisation Clustering (HGFC) algorithm
could greatly improve the outcome
‣introduce metadata into the model for diachronic
analysis

31. References
‣G. Lakoff, J. Espenson, and A. Schwartz, “The
master metaphor list,” University of California at
Berkeley, Tech. Rep., 1991.
‣W. Roberts and M. Egg. "A Comparison of Selectional
Preference Models for Automatic Verb Classification."
Proceeding of EMNLP. 2014.
‣E. Shutova and L. Sun, “Unsupervised metaphor
identification using hierarchical graph factorization
clustering.” in HLT-NAACL, 2013, pp. 978–988.
‣E. Shutova, “Design and evaluation of metaphor
processing systems,” Computational Linguistics,
2015.