Mapping Historical Documents to Geographical Space
1st Takumi Hirayama

Hidetsugu Nanba

Toshiyuki Takezawa

Hiroshima City University
3-4-1 Ozukahigashi, Asaminamiku
Hiroshima 731-3194 JAPANhirayama@ls.info.hiroshima-cu.ac.jp

Hiroshima City University
3-4-1 Ozukahigashi, Asaminamiku
Hiroshima 731-3194 JAPANnanba@hiroshima-cu.ac.jp

Hiroshima City University
3-4-1 Ozukahigashi, Asaminamiku
Hiroshima 731-3194 JAPANtakezawa@hiroshima-cu.ac.jp

ABSTRACT
Geotagging is the process of recognizing place and facility names
in a document, and assigning each set of latitude and longitude
values. In the latter step, an external geographic database, whichcontains pairs of place/facility names and latitude/longitude
values, is used. However, if former place/facility names are used
in a historical document, it is impossible to assign latitude andlongitude values to them, even though their current names arelisted in the database. Furthermore, if there are multiple identicalplace/facility names in the geographical database, we will have tochoose the correct one. In this paper, we propose a method toconstruct a database that contains current and former place/facility
name pairs. We applied a machine learning-based information
extraction method to some text corpora, and automaticallyextracted current and former place/facility name pairs. We also
propose a method that disambiguates the same place/facility
names. We conducted some experiments to confirm theeffectiveness of our method.

CCS Conceptsl

Information systems~Geographic information systemslComputing methodologies~Natural language processinglComputing methodologies~Information extraction

Keywords
Mapping; Information Extraction; Geotagging; Place Name
Disambiguation

1. INTRODUCTION
In this paper, we construct a system that maps historical
documents to geographical space. Our task is considered as a kindof geotagging, which maps documents to geographical space. In
general, geotagging consists of two steps: (1) recognizing placeand facility names in a document, and (2) assigning each latitude
and longitude values. Step 2 has the following two problems. The
first problem is the ambiguity of place/facility names. If the sameplace/facility names extracted in Step 1 are used in different areas,
we will have to disambiguate the names. The second problem is,
if former place/facility names are used in a historical document, it
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MOBIQUITOUS '16 Adjunct Proceedings, November 28-December 01,
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DOI: http://dx.doi.org/10.1145/3004010.3004028

is impossible to assign latitude and longitude values to them, eventhough their current names are listed in the database.
For the first problem, we propose a method that disambiguates
facility/place names using the contextual information of eachname in a document. For the second problem, we construct a
database that contains current and former place/facility name pairs.
We applied a machine learning-based information extraction
method to some text corpora, and automatically extracted current
and former place/facility name pairs.
The remainder of the paper is structured as follows. In Section 2,
we discuss related work. In Section 3, we propose a geotagging
method and a place/facility name disambiguation method. We
conducted some experiments to confirm the effectiveness of our
method. The experimental results are reported in Section4. Finally,
Section 5 concludes our work.

2. RELATED WORK
Lieberman et al. [3] proposed a method that identifies place
names in a document using manually created rules. We use amachine learning-based named entity recognizer CaboCha.
Zhao et al. [6] proposed an algorithm, GeoRank, for thedisambiguation of place/facility name. The basic idea of thisalgorithm is based on PageRank [1], which is well-known as aranking algorithm for Web search. A candidate for an ambiguous
place/facility name can give more evidence to the one near to it ina document, and a location can give more evidence to the one near
to it in the geographic context. Therefore, they constructed amatrix involving all locations, whose values are scores of eachplace/facility of each candidate voted by other ones that belong
different candidates. Our method for the disambiguation also
employs the voting, but is simpler and faster than GeoRank,
because we did not use PageRank.

3. GEOTAGGING
3.1 Construction of a Former/Current PlaceName Dictionary
3.1.1 Basic Strategy
In some documents, both former and current place/facility namepairs are described in the same sentence. The following sentence
is such an example.
[Original]gmA,3% 5=g4)`·t*#)¸

Wf

Z`*

[Translation]
This year, Micronesia tour visits Pohnpei Island (formerly
known as Ponape Island) in the Pacific Ocean.

[Original]In this sentence, the underlined and dashed line phrases indicate
current and former place names, respectively. Therefore, we canextract the current and former place name pairs from this sentence
using the following linguistic pattern.

gmA,3% 5=g Wf Z`<NEW>*4)`</NEW>·t<OLD>*#)</OLD>¸[Original][Translation]“[current name]·t[former name]”

This year, the Micronesia tour visits <NEW>Pohnpei
Island</NEW>(formerly
known
as<OLD>Ponape
Island</OLD>) in the Pacific Ocean.

[Translation]“[current name] (formerly known as [former name]”
However, there are several cases that do not follow the above
pattern, such as shown in the following examples.
(Example 1)[Original]T¹Kº

d(tNdV]d)[Translation]
Saitama district 5: Saitama City (Formerly known as Urawa
City and Omiya City)(Example 2)[Original]
\OMj²l·t§¤¸[Translation]
Act on Specified Commercial Transactions (formerly known
as Mail order law)
In example 1, one current and two former names appear in thesame sentence. In this case, if we apply the above linguistic
pattern to this sentence, we cannot extract the latter former name(“V]d” (Omiya City)). In example 2, the names given before
and after the expression of the linguistic pattern “(t” (formerly
known as) are not place names. Therefore, we apply a machinelearning-based information extraction approach, instead of simply
applying the linguistic pattern. We define this task as a sequencelabeling problem, and apply conditional random field (CRF) as amachine-learning method, which is widely used in various natural
language processing tasks, such as part of speech tagging [2] andnamed entity recognition [4]. We will describe our approach indetail in the following section.

3.1.2 Automatic Annotation of NEW/OLD Tags
We prepare sentences that contain an expression “ · t ”((formerly known as), and manually annotate these sentences with
NEW and OLD tags. Here, NEW and OLD tags indicate current
and former location/facility names, respectively. The following
are examples of manually tagged sentences.

We used these tagged sentences for training and evaluation
purposes using CRF. To employ CRF, we converted texts intomorphological features as 1-4 grams of word, parts of speech, andnamed entities. To obtain these features, we used a Japanese
dependency parser CaboCha. 1 CaboCha can annotate several
named entity tags: “LOCATION,” “ORGANIZATION,”“PERSON,” “DATE,” “TIME,” “ARTIFACT,” “PERCENT,” and“MONEY.” Among them, we used “LOCATION,” “ARTIFACT,”and “ORGANIZATION” for our task. Figure 1 shows how NEW
and OLD tags are used in annotating using CRF. In this figure, we
do not show 2-4 gram features because of the limitation of thepages. In Figure 1, we attempt to identify a tag for a target word
“ ` ” (island). To identify this tag, we use features appearing
before and after this target word.

3.2 Application of a Former/Current PlaceName Dictionary to Documents
Our system identifies all former place/facility names in ahistorical document using CaboCha and replaces them with thecorresponding current names. We explain this procedure using an
example in Figure 2. First, our system identifies all place/facility
names in a document. As a result, “8­d” (Yono City) and “V] ¦ L Y  ³ ” (Omiya Red Cross Hospital) were identified.
Second, we compare these names with all OLD item records inthe dictionary in Section 3.1. If we find the same record, then we
replace the name in the document with the corresponding current
name. In case of the example in Figure 2, “8­d” (Yono City)and “V]¦LY³” (Omiya Red Cross Hospital) are replacedwith “ d” (Saitama City) and “ ¦LY³”(Saitama Red Cross Hospital), respectively.

3.3 Disambiguation of Place/Facility Names
Geotagging is performed in this step. For each place/facility namein the document, we assign latitude/longitude values using several
gazetteers. As gazetteers for geotagging, we used 1,957 prefecture
and municipality names provided by the Geospatial Information1

https://taku910.github.io/cabocha/

Figure 2. Replacing former place/facility names with current place/facility names

Figure 3. Disambiguation of place/facility names
2

Authority of Japan, 117,061 town/area names provided by
Gengo-Shigen-Kyokai,3 and 44,930 entity names, such as stations
and mountains, obtained sfrom Wikipedia.
If there are multiple candidates for a place/facility name, that is,multiple records include the same names as in gazetteers, we will
have to choose the correct record from these candidates. In thefollowing, we explain our method of disambiguating place/facility
names using Figure 3. Our method consists of the three steps. In
the first step, our method assigns latitude/longitude values tononambiguous place/facility names in a document. In Figure 3,“s¡ER” (Hibiya Park) , “k}” (Hikone), and “q¡”(Hibundani) are nonambiguous names. In the second step, we
count the number of prefectures for each record according tononambiguous place/facility name. The purpose of this step is toestimate the geographical area of the document. As “  ¡ ”(Shibuya) appears in Chiba, Tokyo, and Kanagawa prefectures,2

http://www.gsi.go.jp/ENGLISH/index.html

3

http://www.gsk.or.jp/en/

we consider that “¡” (Shibuya) is an ambiguous phrase, andeliminate it from the prefecture count. As a result, we can estimatethat geographical area of this document is “ y < ” (Tokyo),because its frequency 2 is higher than that of “¥” (Shiga). In
the final step, we chose one candidate with a geographical areathat matches the prefecture in the previous step. In Figure 5, wechose “y<¬¡K¡” (Shibuya, Shibuya Ward, Tokyo) as
the corresponding record.

4. EXPERIMENTS
4.1 Extraction of Former/Current NamePairs
4.1.1 Data Sets and Experimental Settings
We randomly selected 6,914 sentences containing the expression“·t” (“(formerly known as”) in the NTCIR-5 Web database [5]dataset and manually labelled these sentences with 3,958 NEW
tags and 4,002 OLD tags. We employed CRF as a machinelearning technique and performed a fivefold cross-validation
using the recall, precision, and F-measure evaluation measures.

We compared these results with the results from a baseline
method that extracts phrases appearing before and after alinguistic pattern “·t” as NEW/OLD pairs.

Shaded phrases in Table 5 indicate mistakenly extracted
current/former name pairs.

4.1.2 Experimental Results and Discussion

Table 4. Extraction results from NTCIR5Web

Table 1 shows the results of the experiment. The table shows thatall values in our method exceed 0.8. In particular, the precision
scores are approximately 0.9. In addition, our method obtained ahigh F-measure compared with the baseline method. Our method
could not extract some NEW/OLD pairs, such as general store
name pair “(/” (PLAZA) and “$(/” (Sonipla). In caseof this pair, our method could not assign either NEW or OLD tags.
Another typical error in our method is “ ;  µ ” (Futaba
Museum) and“ a 7 £ X ª ” (Kawakami Sadayakko’s
Residence). In this case, “qI
;µ” (Cultural Path
Futaba Museum) should be extracted instead of “;µ” (Futaba
Museum).

Freq

Current (NEW)Former (OLD)

16,061

%4(Geneon)&$ LDC
(Pioneer LDC)

15,834HP

4& (Compaq)

1,429

25(!|i? (GrapeCity Inc.)

qI04"|i?
 (Bunka Orient Corp.)

1,094xd (Itako City)U (Ushibori Town)

eGW¶(Horonaibuto Station)swc~C«(The Industry Club ofWest Japan)¬^R(Tsuzuki Minka-en)

6¶(Mikasa Station)

1,064
1,056

Table 1. Evaluation results for the NEW-OLD pair extraction.
MethodTag

Precision

Our
method

NEW

0.889

0.805

0.845

OLD

0.880

0.823

0.851

NEW

0.572

1.000

0.728

OLD

0.572

0.989

0.725

Baseline

RecallF-measureWe applied our method to the following three corpora (Table 2)
and constructed a former/current name pairs dictionary. NEW and
OLD tags are annotated to the sentences in each of these corpora.
Among the tagged phrases, we eliminated pairs, whose
frequencies are low. We also eliminated very long or very shortphrases. The statistics of the results are shown in Table 3.

1,023

Freq

Current (NEW)Former (OLD)

1,4384 (Congo)

51 (Zaire)

1,107

HP

4& (Compaq)

1,068

J{ (Hokuei Town)

V{ (Daiei Town)836@[®on(Japan Housing FinanceAgency)817

3(Russia)

751

Number of
documents

Size

Publication
years

NTCIR5Web

10 million

1.3 TB

Crawled
2004

in
in

(compressed)

Crawled2009

9.1 GB

1993-2012(uncompressed)
ClueWeb09
Yomiurinewspaper

6.7 million
5.4 million

500 GB588

uµ(Se-movie
theater)´_>yd(Ito City,
Shizuoka Prefecture)@[®Eh(Government Housing
Loan Corporation ofJapan)
'"¨©
(the Union of Soviet
Socialist Republics)¯-5'1
(Sotetsu-mubiru etc.)
Q>¢Q(Province Izu
Province)

Table 6. Extraction results from Yomiuri newspaper
Freq
264
218

Table 3. Statistics of three corpora± ^@[(Nagasawa’s Residence)

Table 5. Extraction results from ClueWeb09

Table 2. Statistics for three corpora
Corpuszwª(Matsumoto’s Residence)

Current (NEW)
9QyJ«(Northeast
China)
4:FNQ(Democratic Republicof the Congo)

Former (OLD)
b(Manchuria)
51(Zaire)121r°(Shinsei
Bank, Limited)

sw±vB°(TheLong-Term Credit Bankof Japan)

68

3(Russia)¨(USSR)

50,13861¬dH(Urban
Renaissance Agency)¬dSpDEP(Urban Development
Corporation)

Tables 4, 5, and 6 show some extraction results, former/current
name pairs with their frequencies, from NTCIR5Web,
ClueWeb09, and the Yomiuri newspaper databases, respectively.

58,.4+5(Myanmar)'1+(Burma)56

4(AEON)

.(JUSCO)

CorpusNumber of NEW
tags

Number of OLD
tags

NTCIR5Web

363,888

348,055

ClueWeb09

745,913

831,412

Yomiuri newspaper

42,505

As an overall trend, we obtained more computer company namepairs, such as “HP” and “  4 &   ” (Compaq), from
NTCIR5Web and ClueWeb09 databases than from the Yomiuri
newspaper database, because more computer-related information
is available on the Web than from news articles. The Yomiuri
newspaper database extracted more administrative corporations
than NTCIR5Web and ClueWeb09.
We compared the pairs from NTCIR5Web with those fromClueWeb09, and found that there are apparent differences
between them. For example, we can find a pair of “@[®on” (Japan Housing Finance Agency) and “@[®Eh”(Government Housing Loan Corporation of Japan) in Table 5.
This pair did not appear in NTCIR5Web, because “@[®on” (Japan Housing Finance Agency) was established in 2007
and this organization did not exist when NTCIR5Web database
was created. This result shows that we can expect to extract
former/current name pairs for each year if we prepare only
document corpora that were published in that year. Using this data,
we might obtain the history of a particular place/facility name.

4.2 Geotagging
In this section, we describe the matching technique experiments ingeotagging place names. Section 4.2.1 describes the experimental
method. Section 4.2.2 provides the experimental results anddiscussion.

4.2.1 Data Sets and Experimental Settings
Fifty randomly selected articles published in 1993, 1998, 2003,
2008, and 2012 in Yomiuri newspaper were used. We obtained
177 place/facility names using CaboCha. Twenty ambiguous
names were identified. We manually disambiguated theseambiguous names and used them for our experiment. We used theprecision evaluation measure. A baseline method was prepared,
which randomly selected candidates to confirm the validity of our
proposed method.

4.2.2 Experimental Results and Discussion
Table 7 shows the experimental results, which demonstrate that
our method outperformed the baseline method.
Table 7. Evaluation results for place/facility namedisambiguation.
Year

Numberof names

Number ofambiguous
names

Baseline

Our
method

1993

22

6

0.727

0.909

1998

41

2

0.951

0.951

2003

31

1

0.968

1.000

2008

31

3

0.900

0.935

52

8

0.840

0.942

35.4

4

0.877

0.947

2012
Averageour method to other historical documents in future, such as theancient documents held by the National Archives of Japan.4

5. CONCLUSIONSIn this paper, we proposed a method to map historical documents
to geographical spaces. A dictionary of former/current
place/facility names was constructed from several corpora using a
CRF-based method. We also proposed a method for
disambiguating place/facility names. We conducted someexperiments, and confirmed that our method obtained F-values ofapproximately 0.85 for former/current names. We also confirmedthat our method obtained a precision score of 0.949 fordisambiguation of place/facility names.
In future, we will apply our method to English documents, andextract former/current name pairs from them. As we did not use
any features restricted to Japanese, we believe that it is easy toapply our method to documents written in any languages.

6. ACKNOWLEDGMENTS
This study was carried out under support of Ministry of Internal
Affairs and Communications' SCOPE (Strategic Information andCommunications R&D Promotion Programme).

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10, 2010.
[4] McCallum, A. and Li, W., Early Results for Named Entity
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[5] Oyama, K., Takaku, M., Ishikawa, H., Aizawa, A., andYamana, H., Overview of the NTCIR-5 WEB Navigational
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Unfortunately, only four of the 177 were ambiguous names andwe could not confirm the effectiveness of our former/current
place/facility name dictionary in this experiment. We will apply4

http://www.archives.go.jp/