=Paper=
{{Paper
|id=Vol-1173/CLEF2007wn-GeoCLEF-LevelingEt2007
|storemode=property
|title=University of Hagen at GeoCLEF 2007: Exploring Location Indicators for Geographic Information Retrieval
|pdfUrl=https://ceur-ws.org/Vol-1173/CLEF2007wn-GeoCLEF-LevelingEt2007.pdf
|volume=Vol-1173
|dblpUrl=https://dblp.org/rec/conf/clef/LevelingH07a
}}
==University of Hagen at GeoCLEF 2007: Exploring Location Indicators for Geographic Information Retrieval==
https://ceur-ws.org/Vol-1173/CLEF2007wn-GeoCLEF-LevelingEt2007.pdf
University of Hagen at GeoCLEF 2007:
Exploring Location Indicators for
Geographic Information Retrieval
Johannes Leveling and Sven Hartrumpf
Intelligent Information and Communication Systems (IICS)
University of Hagen (FernUniversität in Hagen)
58084 Hagen, Germany
firstname.lastname@fernuni-hagen.de
Abstract
Location indicators are text segments from which a geographic scope can be inferred, e.g.
adjectives, demonyms (names for inhabitants of a place), geographic codes, orthographic vari-
ants, and abbreviations can be mapped to location names in one or more inferential steps. In
this paper, the normalization of location indicators and treating morphology of location in-
dicators for geographic information retrieval (GIR) within the system GIRSA (Geographic
Information Retrieval by Semantic Annotation) are explored.
Several retrieval experiments are performed on the German GeoCLEF 2007 data, including
a baseline IR experiment on stemmed text (0.119 mean average precision, MAP). Results for
this experiment are compared to results for experiments with normalized location indicators.
Additionally, the latter approach was combined with an approach using semantic networks for
retrieval (an extension of an experiment performed for GeoCLEF 2005).
When using the topic title and description, the best performance was achieved by the com-
bination of approaches (0.196 MAP); adding location names from the narrative part increased
MAP to 0.258. Results indicate that 1) employing normalized location indicators improves
MAP and increases the number of relevant documents found; 2) additional location names
from the narrative increase MAP and recall, and 3) the semantic network approach has a high
initial precision and even adds some relevant documents which were previously not found.
For bilingual (English-German) experiments, queries were first translated into German be-
fore utilizing the translation as input to GIRSA. Performance for these experiments is gener-
ally lower, but reflect results for monolingual German. The baseline experiment (0.114 MAP)
is clearly outperformed by all other experiments, achieving the best performance for a setup
using title, description, and narrative (0.209 MAP).
Categories and Subject Descriptors
H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing—Indexing methods; Linguis-
tic processing; H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—Query
formulation; Search process; H.3.4 [Information Storage and Retrieval]: Systems and Software—Per-
formance evaluation (efficiency and effectiveness)
General Terms
Experimentation, Measurement, Performance
Keywords
Location Indicators, Geographic Information Retrieval
1 Introduction
Traditional information retrieval applies stemming to all words in a text. In the context of geographical
information retrieval (GIR) on textual information, named entity recognition and classification play an
important role to identify location names and to avoid stemming them. GIR is concerned with facilitat-
ing geographically-aware retrieval of information. This awareness often results from identifying proper
nouns in the text, disambiguating them further into person names, organization names, and location names
(geographic entities). Thus, identification of location names is typically restricted to proper nouns only.
The main goal of this paper is to investigate if one should aim at a broader GIR approach which is not
solely based on proper nouns corresponding to location names. To test end, the notion of location indicators
is introduced and retrieval experiments are performed by the system GIRSA (Geographic Information
Retrieval by Semantic Annotation).1 The experiments are based on documents and topics for GeoCLEF
2007, the geographic information retrieval task at CLEF 2007 (Cross Language Evaluation Forum).
2 Location Indicators
2.1 Definition
In this paper, location indicators are investigated. Location indicators are text segments from which the
geographic scope of a document can be inferred. They include, but are not limited to:
• Adjectives corresponding to a location.
Examples: “tunesisch”/“Tunisian” for “Tunesien”/“Tunisia”; “irisch”/“Irish” for “Irland”/“Ire-
land”; “bayrisch, bayerisch”/“Bavarian” for “Bayern”/“Bavaria”.
• Demonyms, e.g. the name for inhabitants originating from a location.
Examples: “Franzose, Französin”/“Frenchman, Frenchwoman” for “Frankreich”/“France”; “Mon-
gole, Mongolin”/“Mongolian” for “Mongolei”/“Mongolia”; “Düsseldorfer, Düsseldorferin”/“in-
habitant of Düsseldorf” for “Düsseldorf”.
• Codes for a location name, including ISO region codes, postal and zip codes.
Examples: “HU21” for “Tolna County, Hungary” (FIPS region code); “GUY” for “Guyana” (ISO
3166-1 alpha-3); “GY” for “Guyana” (ISO 3166-1 alpha-2); “EGLL” for “Heathrow Airport, Lon-
don, UK” or “LPBJ” for “Beja Air Base, Beja, Portugal” (International Civil Aviation Organization
codes).
• Abbreviations and acronyms for a location name, including abbreviations of adjectives.
Examples: “franz.” for “französisch”/“French” (mapped to “Frankreich”/“France”); “ital.” for
“italienisch”/“Italian” (“Italien”/“Italy”); “Whv.” for “Wilhelmshaven”; “NRW” for “Nordrhein-
Westfalen”/“North Rhine-Westphalia”.
• Orthographic variants, including exonyms and historic names.
Examples: “Cologne” for “Köln”; “Lower Saxony” for “Niedersachsen”.
• Language names in the text.
Example: “Portuguese” for “Portuguese speaking countries” (mapped to “Portugal, Angola, Cape
Verde, East Timor, Mozambique, and Brazil”).
1 The research described is part of the IRSAW project (Intelligent Information Retrieval on the Basis of a Semantically Annotated
Web; LIS 4 – 554975(2) Hagen, BIB 48 HGfu 02-01), which is funded by the DFG (Deutsche Forschungsgemeinschaft).
• Meta-information for a document, i.e. the language a document is written in.
Example: “Die Katze jagt die Maus” for “German language” (mapped to “Germany, Austria, and
Switzerland”).
• Unique entities associated with a geographic location, i.e. headquarters of an organization, persons,
and buildings.
Examples: “Boeing” for “Seattle, Washington”; “Moliére” for “France”; “Galileo Galilei” for
“Italy”; “Eiffel Tower” for “Paris”; “Pentagon” for “Washington, D.C.”.
• The location names itself, including full names and short forms.
Example: “Republik Korea”/“Republic of Korea” for “Südkorea”/“South Korea”.
Typically, location indicators are not included in gazetteers, e.g. the morphology and lexical knowl-
edge for adjectives is missing completely. Distinct location indicators contribute differently to the task of
assigning a geographic scope to a document. Their importance depends on their usage and frequency in
the corpus (e.g. adjectives are generally frequent) and the correctness of identifying them, because new
ambiguities arise (e.g. the ISO 3166-1 code for Tuvalu (TV) is also the abbreviation for television).
2.2 Location Indicator Normalization
The normalization of location indicators to location names takes place on different levels of linguistic
analysis in GIRSA.
• Character level: In all entries of the name lexicons, diacritical marks are replaced with non-accented
characters to create orthographic variants of names. These resulting orthographic variants are used as
elements of a synonym set and normalized by selecting a representative for the synonym set (synset).
Example: “Québec” →“Quebec”.
• Morphologic level: Inflectional endings for adjective and noun forms are identified and separated
using a set of manually created rules and large lists of exceptions. Typical German inflectional
endings of a word form (e.g. “-s”, “-es”, “-er”, “-en”, “e”) are removed before the lookup in name
lexicons. (Note that location names usually do not have a plural form.)
More complex cases are multi-word expressions which may contain inflectional morphology. Mor-
phologic variations of location names are reduced to its base form.
Examples: “Berlins” →“Berlin”; “das Rote Meer” →“Rote Meer”; “des Roten Meer(e)s” →“Rote
Meer”.
Derivational morphology is part of connecting adjectives to location names.
Example: “bayrisch” →“Bayern”; “dänisch” →“Dänemark”.
• Semantic level: Prefixes indicating compass directions are separated from the name. A database
management system may view the hyphenated result as either one or two terms, depending on the
search options. Thus, a search for “Norddeutschland” will also return documents containing the
phrase “im Norden Deutschlands”. Also on the semantic level, a mapping between location indica-
tors and location names takes place.
Examples: “Norddeutschland” →“Nord-Deutschland”; “Süd-Frankreich” →“Süd-Frankreich”;
exception: “Südafrika” →“Südafrika”.
• Lexical level: Name variations are normalized using synset representatives. The synsets contain
elements referencing the same geographic location.
Example: “Burma”, “Birma” →“Myanmar”.
Of course, there is an implicit ordering of normalization steps: morphological variations are identi-
fied first, removing inflectional endings before lookup. Then, complex named entities are recognized and
represented as a single term. Next, adjectives and acronyms are mapped to the expanded location name.
Normalization by mapping to a synset representative is the last operation.
2.3 Semantic Analysis for GIR
This year, the approach of semantic representation matching (GIR-InSicht, derived from the deep QA
system InSicht, [5]) was tried again for GeoCLEF. See [8] for details on the first experiment in this direction
at GeoCLEF 2005. GIR-InSicht matches reduced semantic representations of the topic description (or topic
title) to the semantic representations of sentences from the document collection. This process is quite strict
and proceeds sentence by sentence.2 Before matching starts, the query semantic network was allowed to
be split in parts at specific semantic relations, e.g. at a LOC relation (location of a situation or object) of
the MultiNet formalism (multilayered extended semantic networks; [6]), to increase recall while not losing
too much precision.
For GeoCLEF 2007, query decomposition was implemented, i.e. a query can be decomposed into two
dependent queries, the subquery and the main query. The subquery was answered by the QA system In-
Sicht; the answers were integrated into the main query on the semantic network level (thereby avoiding the
complicated or problematic integration on the surface level). For example, the title of topic 10.2452/57-GC
“Whiskyherstellung auf den schottischen Inseln” (‘Whiskey production on the Scottish Islands’) and simi-
larly the description of this topic lead to the subquery “Nenne schottische Inseln” (‘Name Scottish islands’).
Decomposition is also applied to the alternative query semantic networks derived by inferential query ex-
pansion. In the above example, this leads to the subquery “Nenne Inseln in Schottland” (‘Name islands in
Scotland’). InSicht answers the subqueries on the semantic representations of the GeoCLEF document col-
lection and the German Wikipedia. For the above subqueries, it correctly delivered islands like “Iona” and
“Islay”, which in turn lead to main query semantic networks which could be paraphrased as “Whiskyher-
stellung auf Iona” (‘Whiskey production on Iona’) and “Whiskyherstellung auf Islay” (‘Whiskey production
on Islay’). Note that the decomposed queries are processed only as alternatives to the original query.
Another decomposition strategy produces questions aiming at meronymy knowledge based on the ge-
ographical type of a location, e.g. for a country C in the original query a subquery like “Name cities in
C.” is generated, whose results are integrated into the main query semantic network. This strategy led to
interesting questions like “Welcher Staat/Welche Region/Welche Stadt liegt im Himalaya?” (‘Which coun-
try/region/city is located in the Himalaya?’). In total, both decomposition strategies led to 80 different
subqueries for the 25 topics. After the title and description of a topic have been processed independently,
GIR-InSicht combines the results. If a document occurs in the title results and the description results, the
highest score was taken for the combination.
The semantic matching approach is completely independent of the main approach in GIRSA. Some
of the functionality of the main approach is also realized in the matching approach, e.g. some of the
location indicators described above are also exploited in GIR-InSicht (adjectives; demonyms for regions
and countries). They are not normalized, but the query semantic network is extended by many alternative
semantic networks that are in part derived by symbolic inference rules using the semantic knowledge about
location indicators. In contrast, the main approach exploits this information on the level of terms.
2.4 Related Work
Nagel [11] describes the manual construction of a place name ontology containing 17,000 geographic
entities as a prerequisite for analyzing German sentences. He states that in German, toponyms have a
simple inflectional morphology, but a complex (idiosyncratic) derivational morphology.
Buscaldi, Rosso et al. [1] investigate semi-automatic creation of a geographical ontology, using gazetteer
data and resources like Wikipedia and WordNet.
Wang et al. [12] introduce the concept of dominant locations (later called implicit locations, [10]).
Implicit locations are locations not explicitly mentioned in a text. The only case explored are locations that
are closely related to other locations.
Previous work on GIR by members of the IICS includes experiments with documents and queries
represented as semantic networks [8], and experiments dealing with linguistic phenomena, such as cases of
regular metonymy of location names [7], which was utilized as a means to increase precision in GIR. Due
2 But documents can also be found if the information is distributed across several sentences because a coreference resolver pro-
cessed all document representations.
to time constraints, metonymy recognition was not included in GIRSA. For the experiments for GeoCLEF
2007, we focused on investigating means to increase recall.
3 Experimental Setup
The GeoCLEF 2007 documents constitute a corpus of more than 275,000 German newspaper articles from
‘Frankfurter Rundschau’, ‘Schweizerische Depeschenagentur’, and ‘Der Spiegel’ from the years 1994 and
1995 (see [3]). The performance of GIRSA is evaluated on the test set from GeoCLEF 2007, containing
25 topics with a title, a short description, and a narrative part. As in a setup for previous GIR experiments
on GeoCLEF data [8, 9], the documents were indexed with the Zebra database management system [4],
which supports a standard relevance ranking (tf-idf IR model).
Documents are preprocessed as follows to produce different indexes:
1. S: As in traditional IR, all words in the document text (including location names) are stemmed, using
an implementation of the German snowball stemmer.
2. SL: Location indicators are identified and normalized to a base form of a location name.
3. SLD: In addition, decompounding is applied to the words in the text. German decompounding
follows the frequency-based approach described in [2].
4. O: Documents and queries are represented as semantic networks and GIR is seen as a form of ques-
tion answering (see Sect. 2.3).
The following location indicators were normalized in documents and queries for the GIR experiments:
adjectives corresponding to locations, demonyms, abbreviations, orthographic variants, language names,
and location names. Normalization consists of applying a set of transformation rules (covering regular
variations) and looking up locations in specialized exception lists for each type of location indicator.
Basically, queries and documents are processed in the same way. The title and short description were
used for creating a query. GeoCLEF topics contain a narrative part describing documents which are to
be assessed as relevant. Instead of employing a large gazetteer containing location names as a knowledge
base for query expansion, additional location names were extracted from the narrative part of the topic. No
meronymy information is utilized for direct query expansion (because these may be just the terms the blind
feedback may find and there would be a combined effect).
For the bilingual (English-German) experiments, the queries were translated using the Promt web ser-
vice for machine translation.3 Query processing then follows the setup for monolingual German experi-
ments.
The following parameter settings were used in different retrieval experiments:
1. query language: German (DE) or English (EN);
2. index type: stemming only (S), identification of locations, not stemmed (SL), decomposition of
German compounds (SLD), hybrid (SLD/O), and based on semantic networks (O);
3. query fields: combinations of title (T), description (D), and locations from narrative (N).
Parameters and results for monolingual German and bilingual English-German experiments are shown
in Table 1. The table shows relevant and retrieved documents (rel ret), MAP and precision at five, ten,
and twenty documents. In total, 904 documents were assessed as relevant for the 25 topics. For the
run FUHtd6de, results from GIR-InSicht were merged with results from the experiment FUHtd3de in a
straightforward way, using the maximum score.
3 http://www.e-promt.com/
Table 1: Results for different retrieval experiments on German GeoCLEF 2007 data.
Run Parameters Results
query language index fields rel ret MAP P@5 P@10 P@20
FUHtd1de DE S TD 597 0.119 0.280 0.256 0.194
FUHtd2de DE SL TD 707 0.191 0.288 0.264 0.254
FUHtd3de DE SLD TD 677 0.190 0.272 0.276 0.260
FUHtdn4de DE SL TDN 722 0.236 0.328 0.288 0.272
FUHtdn5de DE SLD TDN 717 0.258 0.336 0.328 0.288
FUHtd6de DE SLD/O TD 680 0.196 0.280 0.280 0.260
GIR-InSicht DE O TD 52 0.067 0.104 0.096 0.080
FUHtd1en EN S TD 490 0.114 0.216 0.188 0.162
FUHtd2en EN SL TD 588 0.146 0.272 0.220 0.196
FUHtd3en EN SLD TD 580 0.145 0.224 0.180 0.156
FUHtdn4en EN SL TDN 622 0.209 0.352 0.284 0.246
FUHtdn5en EN SLD TDN 619 0.188 0.272 0.256 0.208
Precision
0.60
3 FUHtd1de: 0.119 MAP
2 FUHtd3de: 0.190 MAP
? FUHtd6de: 0.196 MAP
0.50 2
? × GIR-InSicht: 0.067 MAP
0.40 3
?
2 ?
2
?
0.30 3 2
3 ?
2
0.20 2
?
× 3
× 2
?
0.10 × × 3 2
?
× 3 3 3 2
?
3
× × × × 2
?
×
3 ×
2?
0.00 3
0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Recall
Figure 1: Recall-precision graph for German monolingual GeoCLEF experiments.
Table 2: Comparison of results per topic for different German monolingual runs.
FUHtd3de GIR-InSicht FUGtd6de
topic rel ret/rel MAP rel ret/rel MAP rel ret/rel MAP
10.2452/56-GC 2/ 5 0.002 1/ 5 0.029 2/ 5 0.030
10.2452/57-GC 1/ 1 0.200 1/ 1 1.000 1/ 1 0.200
10.2452/61-GC 45/ 46 0.530 21/ 46 0.305 45/ 46 0.492
10.2452/65-GC 83/ 95 0.348 3/ 95 0.007 83/ 95 0.291
10.2452/66-GC 1/ 2 0.017 0/ 2 0.000 1/ 2 0.017
10.2452/68-GC 203/269 0.380 0/269 0.000 203/269 0.380
10.2452/69-GC 20/ 45 0.123 2/ 45 0.006 20/ 45 0.111
10.2452/70-GC 17/ 23 0.115 0/ 23 0.000 17/ 23 0.115
10.2452/72-GC 4/ 10 0.022 3/ 10 0.160 7/ 10 0.208
10.2452/75-GC 83/110 0.440 21/114 0.173 83/110 0.460
4 Results and Discussion
Identifying and indexing normalized location indicators, decompounding, and adding location names from
the narrative part improves performance considerably, i.e. 120 additional relevant documents are found and
MAP is increased from 0.119 (FUHtd1de) to 0.258 (FUHtdn5de) in comparison to the baseline experiment.
Decompounding German nouns seems to have different effects on precision and recall (FUHtd2de
vs. FUHtd3de and FUHtdn4de vs. FUHtdn5de): while more relevant documents are retrieved without
decompounding, initial precision is higher when utilizing decompounding.
Topic 10.2452/55-GC contains a negation in the topic title and description (“but not in the Alps”).
However, adding the location names from the narrative part of the topic (“Scotland, Norway, Iceland”) did
not notably improve precision for this topic (0.005 MAP in FUGtd3de vs. 0.013 MAP in FUHtdn5de).
A small analysis of results found by GIR-InSicht in comparison with the main GIR system reveals that
for ten topics, GIR-InSicht retrieved documents, and for seven topics, it returned relevant documents (see
Fig. 1 and Table 2). This approach, originating from question answering and based on a strict matching
of semantic representations, returns three additional relevant documents for the combination (FUHtd6de).
However, the MAP for some topics in the combined run indicates that merging by taking the maximum
of two scores might be too simple. For a single topic (10.2452/52-GC), zero relevant documents were
retrieved in all experiments.
Results for the bilingual (English-German) experiments are generally lower. As for German, all other
experiments outperform the baseline (0.114 MAP). The best performance is achieved by an experiment us-
ing topic title, description, and location names from the narrative (0.209 MAP). In comparison with results
for the monolingual German experiments, the performance drop lies between 4.2% (first experiment) and
27.1% (fifth experiment).
5 Conclusion and Outlook
In this paper location indicators were introduced as text segments from which location names can be in-
ferred. For the GeoCLEF 2007 experiments, different indexes containing stemmed words and location
indicators normalized to location names were created. Results of the GIR experiments show that MAP
is higher when using location indicators instead of location names to represent the geographic scope of a
document. A broader approach to identify the geographic scope of a document is needed because proper
nouns or location names do not alone imply the geographic scope of a document.
In addition, we investigated using location names extracted from the narrative part of a topic (instead
of looking up additional location names in large gazetteers). The narrative contains a detailed description
about which documents are to be assessed as relevant (and which not), including additional location names.
Adding these location names to the query notably improves performance. This result is seemingly in con-
trast to some results from GeoCLEF 2006, were it was found that additional query terms (from gazetteers)
degrade performance. A possible explanation is that in this experiment, only a few location names were
added (3.16 location names on average for fifteen of the 25 topics with a maximum of thirteen additional
location names). When using a gazetteer, one has to decide which terms are the most useful in query ex-
pansion. If this decision is based on the importance of a location, a semantic shift in the results may occur,
which degrades performance. In contrast, selecting terms from the narrative part increases the chance to
expand a query with relevant terms only.
The hybrid approach for GIR proved interesting, and even a few additional relevant documents were
found in the combined run. As GIR-InSicht originates from a deep (read: semantic) QA approach, it returns
documents with a high initial precision, which may prove useful in combination with a geographic blind
feedback strategy. GIR-InSicht performs worse than the IR baseline, because only 102 documents were
retrieved for ten of the 25 topics. However, more than half (56 documents) turned out to be relevant.
Several improvements are planned for GIRSA. These include using estimates for the importance (weight)
of different location indicators, possibly depending on the context (e.g. “Danish coast” →“Denmark”,
but “German shepherd” 6→ “Germany”), and using a part-of-speech tagger and named entity recognizer
to identify location names. Finally, we plan to investigate the combination of means to increase preci-
sion (e.g. recognizing metonymic location names) with means to increase recall (e.g. recognizing and
normalizing location indicators).
References
[1] Davide Buscaldi, Paolo Rosso, and Piedachu Peris Garcia. Inferring geographical ontologies from
multiple resources for geographical information retrieval. In Proceedings of the 3rd Workshop on
Geographical Information Retrieval (GIR 2006), pages 52–55, Seattle, USA, 2006.
[2] Aitao Chen. Cross-language retrieval experiments at CLEF 2002. In Carol Peters, Martin Braschler,
Julio Gonzalo, and Michael Kluck, editors, Advances in Cross-Language Information Retrieval, Third
Workshop of the Cross-Language Evaluation Forum, CLEF 2002, volume 2785 of LNCS, pages 28–
48, Berlin, 2002. Springer.
[3] Fredric Gey, Ray Larson, Mark Sanderson, Hideo Joho, Paul Clough, and Vivien Petras. GeoCLEF:
the CLEF 2005 cross-language geographic information retrieval track overview. In Carol Peters,
Fredric C. Gey, Julio Gonzalo, Henning Müller, Gareth J. F. Jones, Michael Kluck, Bernardo Magnini,
and Maarten de Rijke, editors, Accessing Multilingual Information Repositories, 6th Workshop of the
Cross-Language Evaluation Forum, CLEF 2005, volume 4022 of LNCS, pages 908–919. Springer,
Berlin, 2006.
[4] Sebastian Hammer, Adam Dickmeiss, Heikki Levanto, and Mike Taylor. Zebra – User’s Guide and
Reference. Copenhagen, Denmark, 2005.
[5] Sven Hartrumpf and Johannes Leveling. University of Hagen at QA@CLEF 2006: Interpretation
and normalization of temporal expressions. In Alessandro Nardi, Carol Peters, and José Luis Vicedo,
editors, Results of the CLEF 2006 Cross-Language System Evaluation Campaign, Working Notes for
the CLEF 2006 Workshop, Alicante, Spain, 2006.
[6] Hermann Helbig. Knowledge Representation and the Semantics of Natural Language. Springer,
Berlin, 2006.
[7] Johannes Leveling and Sven Hartrumpf. On metonymy recognition for GIR. In Proceedings of the
3rd Workshop on Geographical Information Retrieval (GIR 2006), pages 9–13, Seattle, USA, 2006.
[8] Johannes Leveling, Sven Hartrumpf, and Dirk Veiel. Using semantic networks for geographic infor-
mation retrieval. In Carol Peters, Fredric C. Gey, Julio Gonzalo, Gareth J. F. Jones, Michael Kluck,
Bernardo Magnini, Henning Müller, and Maarten de Rijke, editors, Accessing Multilingual Informa-
tion Repositories, 6th Workshop of the Cross-Language Evaluation Forum, CLEF 2005, volume 4022
of LNCS, pages 977–986. Springer, Berlin, 2006.
[9] Johannes Leveling and Dirk Veiel. University of Hagen at GeoCLEF 2006: Experiments with
metonymy recognition in documents. In Alessandro Nardi, Carol Peters, and José Luis Vicedo, edi-
tors, Results of the CLEF 2006 Cross-Language System Evaluation Campaign, Working Notes for the
CLEF 2006 Workshop, Alicante, Spain, 2006.
[10] Zhisheng Li, Chong Wang, Xing Xie, Xufa Wang, and Wei-Ying Ma. Indexing implicit locations for
geographical information retrieval. In Proceedings of the 3rd Workshop on Geographical Information
Retrieval (GIR 2006), pages 68–70, Seattle, USA, 2006.
[11] Sebastian Nagel. An ontology of German place names. Corela – Cognition, Représentation, Langage,
Le traitement lexicographique des noms propres (Numéros spéciaux), 2005.
[12] Lee Wang, Chuang Wang, Xing Xie, Josh Forman, Yansheng Lu, Wei-Ying Ma, and Ying Li. Detect-
ing dominant locations from search queries. In Proceedings of the 28th annual international ACM
SIGIR conference on research and development in information retrieval (SIGIR ’05), pages 424–431,
New York, USA, 2005. ACM Press.