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Combining Approaches forIdentifying Metonymy Classes of

Named Locations

Sven Hartrumpf and Johannes Leveling

Intelligent Information and Communication Systems (IICS)University of Hagen (FernUniversität in Hagen)

58084 Hagen, [email protected]

EPIA 2007, Dec. 4, Guimarães, Portugal

http://localhost/var/www/apps/conversion/tmp/scratch_15/[email protected]

http://localhost/var/www/apps/conversion/tmp/scratch_15/[email protected]



IdentifyingMetonymyClasses of

NamedLocations

S. Hartrumpfand

J. Leveling

Introduction

MetonymyClasses forLocationNames

CorpusAnnotationwithMetonymyInformation

MetonymyClassifiers

ClassifierCombination

EvaluationResults

Conclusionand Outlook

References

Outline

1 Introduction

2 Metonymy Classes for Location Names

3 Corpus Annotation with Metonymy Information

4 Metonymy Classifiers

5 Classifier Combination

6 Evaluation Results

7 Conclusion and Outlook

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Figurative Speech

DefinitionMetonymy is a figure of speech in which a speaker uses

one entity to refer to another that is related to it

(Lakoff and Johnson, 1980)

→ senses different from normal reading

→ identifying metonymy can be seen as word sense

disambiguation

→

classification task• levels of classification:

coarse (LITERAL / NON-LITERAL)

medium (LIT / MET / MIX )

fine

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Metonymy

• Typically, metonymy recognition experiments on

English texts

• Growing importance in research and applications:

• SemEval I task at ACL 2007 (Markert and Nissim,2007): recognition of metonymic location andorganization names

• Question Answering (Stallard, 1993),• Machine Translation (Kamei and Wakao, 1992),• Geographic Information Retrieval (Leveling and

Hartrumpf, 2006)

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Metonymy Classes

(Markert and Nissim, 2002)Class Description

Medium Fine

LIT literal literal, geographic sense

MET place-for-event →event

place-for-people:

place-for-gov(ernment) →people in government

place-for-off(icials) →people in official administration

place-for-org(anization) →organization at location

place-for-pop(ulation) →population

place-for-product →product from place

othermet metonymy not covered by regular

pattern

MIX mixed literal and metonymic sense

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Metonymy Classes


Medium Fine



place-for-people:







pattern


Example for literal :

Seit Beginn des Kosovo-Krieges rekrutiert die UCK in DEUTSCHLAND

Kämpfer. – 9951

(Since the beginning of the Kosovo war, the UCK recruits fighters in

GERMANY.)

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Metonymy Classes


Medium Fine



place-for-people:







pattern


Example for place-for-event :

Nach dem KOSOVO geht es in Makedonien und Montenegro weiter. – 6336

(After KOSOVO, it will continue in Macedonia and Montenegro.)

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Metonymy Classes


Medium Fine



place-for-people:


place-for-off(icials) →people in official administrationplace-for-org(anization) →organization at location




pattern


Example for place-for-off :

. . . DEUTSCHLAND (wird) mehr Geschick haben als Clinton. – 2435

(. . . GERMANY will be more successful than Clinton.)

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Metonymy Classes


Medium Fine



place-for-people:






pattern


Example for place-for-product :

Politisch sollte die Unterschrift Belgrads unter RAMBOUILLET erzwungen

werden. – 12087

(The signature of Belgrade under RAMBOUILLET should be forced politically.)

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Metonymy Classes


Medium Fine



place-for-people:






pattern


Example for othermet :

Dabei ist AFRIKA auch bei dieser Zusammenstellung von Musik eher eine

ideelle Klammer. – 8415

(But AFRICA is an ideational cramp for this composition of music, too.)

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Metonymy Classes


Medium Fine



place-for-people:






pattern


Example for mixed :

Die Friedensfahrt gewinnt im Osten DEUTSCHLANDS wieder stark an

Renommee. – 1498

(The peace tour makes a reputation in the eastern part of GERMANY again.)

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Data and Annotation (1/2)

• TüBa-D/Z corpus containing articles from the German

newspaper taz (27,067 sentences with 500,628 tokens)

• Annotation levels:

• (PoS tags)• NE tags (LOC , PER , ORG , and MISC )• NE subclasses (e.g. first names, last names, and other

parts of a name)• Label corresponding to medium and fine metonymy

classification

• Example: token Africa →(NE , LOC , region , MET ,othermet )

→ 1,515 (18.5%) of all toponyms are used in a nonliteral

sense

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Data and Annotation (2/2)

Annotation checking:

• Applied the variation (or inconsistency) detection tool

DECCA (http://decca.osu.edu/)

• Used corrections supplied by the TüBa-D/Z corpus

publishers

• Identify additional spelling errors by frequency analysis

→ Errors in text and on levels of PoS tags, NE tags, NE

subclasses, medium and fine metonymy classes

http://decca.osu.edu/



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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Frequency of Metonymy

Classes

Class Frequency

Coarse Medium Fine

LITERAL LIT literal 6672

NON-LITERAL MET (1433) place-for-event 55

place-for-gov 51

place-for-off 512

place-for-org 148

place-for-pop 340

place-for-product 10

othermet 317

MIX mixed 82

f

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Metonymy Classifiers

• All classifiers are based on a memory-based learner,

TiMBL (supervised learning)

• All classifiers implemented by different people

• Shallow classifier 1 (SC1): relies largely on featuresobtained from gazetteer lookup

• Shallow classifier 2 (SC2): includes features encoding

ontological sorts from the context

• Deep classifier (DC): employs features from parseresults (syntactico-semantic parsing with a semantically

oriented computer lexicon)

Id tif i

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Metonymy Classifier SC1

Main features for training instances:

• 109 features• Character features (e.g. token starts with capital letter?)• Semantic entities (entity classes for the token obtained

from morpholexical analysis)• PoS tags• Gazetteer lookups (for cities, countries, etc.)• Metonymy context (metonymy class of the token to the left)

Identifying

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Metonymy Classifier SC2

Main features for training instances:

• 269 features• Sentence context (lemma and distance to the location

token)• Word context (the first three and the last three characters

of the token, PoS tag, position in the sentence,

upper/lower case information, and word length)• Metonymy context (metonymy class of two preceding

tokens)• Ontological sorts (for words in the context, using a bit

vector representation of a sort hierachy)• Sentence length (number of tokens)

Identifying

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Metonymy Classifier DC (1/2)

Background:

• Syntactico-semantic parser (WOCADI) delivers

features for the deep classifier

• Semantic result: MultiNet (multilayered extendedsemantic networks, Helbig (2006)); MultiNet nodes:

disambiguated word readings (concepts)

• Syntactic result: dependency graph

• Important resource for the parser:semantically oriented lexicon (HaGenLex)

Identifying

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Metonymy Classifier DC (1/2)

• 13 features

• p-quality: quality of the parser result as a numerical value between 500and 1000

• token: name token; type: name type (i.e. lemma)• dep-rel: dependency relation leading to the governor (mother

constituent)• role: semantic role filled by the name• appos-molec: name accompanied by a molecular apposition?• adjective: lemma of modifying adjective• csister-ctype: lemma of coordinated sister node with compound

reduction• csister-entity: semantic entity value of coordinated sister node• mother-entity: semantic entity value of mother constituent• mother-sort: ontological sort of mother constituent• mother-type: type (i.e. lemma) of mother• mother-ctype: type (i.e. lemma) of mother with compound reduction

Identifying

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers


EvaluationResults


References

Classifier Combination

Features for training instances:

• 15 features• results for the location token (from SC1, SC2, DC)

• results for tokens in the context (from SC1, SC2, DC)

Identifying

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NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers

Classifier

Combination

EvaluationResults


References

Results on Coarse LevelClass SC1 SC2 DC

P R P R P R

LITERAL 89.16 93.74 93.36 93.71 94.01 36.71

NON-LITERAL 64.36 49.83 71.81 70.63 82.31 32.87

Identifying

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y gMetonymyClasses of

NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers

Classifier

Combination

EvaluationResults


References

Results on Coarse LevelClass SC1 SC2 DC Combined

P R P R P R P R

LITERAL 89.16 93.74 93.36 93.71 94.01 36.71 95.13 94.83

NON-LITERAL 64.36 49.83 71.81 70.63 82.31 32.87 77.54 78.61

Identifying

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y gMetonymyClasses of

NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers

Classifier

Combination

EvaluationResults


References

Results on Medium LevelClass SC1 SC2 DC

P R P R P R

LIT 88.97 94.18 93.35 93.68 93.80 36.75

MET 63.27 48.08 70.08 68.81 81.76 33.15

MIX 54.29 23.17 22.35 23.17 26.67 4.88

Identifying

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MetonymyClasses of

NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers

Classifier

Combination

EvaluationResults


References

Results on Medium LevelClass SC1 SC2 DC Combined

P R P R P R P R

LIT 88.97 94.18 93.35 93.68 93.80 36.75 94.75 95.23

MET 63.27 48.08 70.08 68.81 81.76 33.15 76.11 77.60

MIX 54.29 23.17 22.35 23.17 26.67 4.88 75.00 18.29

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IdentifyingMetonymy



MetonymyClasses of

NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers

Classifier

Combination

EvaluationResults


References

Effect of Metonymy Support in

the Lexicon

Metonymysupport

Sentenceconstraint

#Sentences Parse results (%)

Full Chunks Failed

no NON-

LITERAL

1,124 47.15 37.46 15.39

no constraint 27,067 54.08 31.09 14.83yes NON-

LITERAL

1,124 52.40 32.21 15.39

no constraint 27,067 53.60 31.19 15.21

IdentifyingMetonymy

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MetonymyClasses of

NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers

Classifier

Combination

EvaluationResults


References

Conclusion and Outlook

• Classifiers differ in their strengths and weaknesses(for example, the deep method shows the highest

precision values, but recall values are low because they

are limited by the parser coverage)

→ Combined classifier outperforms each single classifiersignificantly

• Created a new resource about metonymy in German

• Metonymy support in the lexicon improves results of

syntactico-semantic parser

• Future work: investigate semantic representation of

metonymic names;

application to QA and GIR

IdentifyingMetonymy

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MetonymyClasses of

NamedLocations

S. Hartrumpfand

J. Leveling

Introduction



MetonymyClassifiers

Classifier

Combination

EvaluationResults


References

Selected References

Helbig, Hermann (2006). Knowledge Representation and the Semantics of Natural

Language . Berlin: Springer. URL http://www.springer.com/sgw/cda/

frontpage/0,11855,1-40109-22-72041224-0,00.html .

Kamei, Shin-ichiro and Takahiro Wakao (1992). Metonymy: Reassessment, survey of

acceptability, and its treatment in machine translation systems. In Proceedings of the

30th Annual Meeting of the Association for Computational Linguistics (ACL’92), pp.

309–311. Newark, Delaware.

Lakoff, George and Mark Johnson (1980). Metaphors We Live By . Chicago University

Press.

Leveling, Johannes and Sven Hartrumpf (2006). On metonymy recognition for GIR. In

Proceedings of GIR-2006, the 3rd Workshop on Geographical Information Retrieval

(hosted by SIGIR 2006). Seattle, Washington. URL

http://www.geo.unizh.ch/~rsp/gir06/papers/individual/leveling.pdf.

Markert, Katja and Malvina Nissim (2002). Towards a corpus annotated for metonymies:

The case of location names. In Proceedings of the 3rd International Conference on

Language Resources and Evaluation (LREC 2002). Las Palmas, Spain.Markert, Katja and Malvina Nissim (2007). Task 08: Metonymy resolution at SemEval-07. In

Proceedings of SemEval 2007 .

Stallard, David (1993). Two kinds of metonymy. In Proceedings of the 31st Annual Meeting

of the Association for Computational Linguistics (ACL’93), pp. 87–94. Columbus, Ohio.

URL http://www.aclweb.org/anthology/P93-1012.

http://www.springer.com/sgw/cda/frontpage/0,11855,1-40109-22-72041224-0,00.html



http://www.geo.unizh.ch/~rsp/gir06/papers/individual/leveling.pdf


http://www.aclweb.org/anthology/P93-1012

http://www.aclweb.org/anthology/P93-1012




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