Development of Methods to Extract Place Namesand Estimate Their Places from Web Newspaper
ArticlesShoichiro Hara
Center for Southeast Asian Studies
Kyoto University
Kyoto, Japan
https://orcid.org/0000-0001-9343-1800

Akira Kubo
Graduate School of Arts and Sciences
The Open University of Japan
Chiba, Japan
https://orcid.org/0000-0002-4901-8290

Hirotaka Kameko
Academic Center for Computing andMedia Studies
Kyoto University
Kyoto, Japan
https://orcid.org/0000-0001-9844-6198

Shinsuke Mori
Academic Center for Computing andMedia Studies
Kyoto University
Kyoto, Japan
https://orcid.org/0000-0001-8596-8667

Abstract— Area studies is an interdisciplinary study of thehumanities adjacent to various research fields such as social
sciences, natural sciences, engineering, medicine, and health. If
focusing on humanity sciences, researchers’ primary researchresources have been text media, such as historical records,
literary works, research papers, newspapers, and magazines.
Researchers have engaged in analyses by reading the texts
carefully as part of their research activities. However, with the
spread of the Internet, Web data have become an inevitablesource for area studies. We have explored new directions forarea studies based on informatics compatible with big data andthe Internet age. However, the low accuracy of place nameextraction from texts and their place identification on a map
hinders text processing, making it impossible to analyze big text
data on the Web automatically. Using BiLSTM-CRF and a
Balanced Corpus of Contemporary Written Japanese, ourprevious study realized approximately 0.9 of BiLSTM-CRF
recognition accuracy, which demonstrated the effectiveness of
BiLSTM-CRF. Our method can extract place names properly,
but not enough if it is difficult to correctly identify their placeson the map. Currently, we are trying a simple strategy: We
assume that the locations of place names appearing in the samenews article are close to each other. Thus, we compute theweighted average of all candidate coordinates of all place names
in a news article as the pseudo center, then, as for each place
name, choose the coordinate with the shortest distance from thepseudo center. This paper introduces the above methods andresults in detail.
Keywords— area studies, big data, machine learning, Named
Entity Recognition, Geocoding, LDA, KyTea, BiLSTM-CRF

I. INTRODUCTION
Area studies is an interdisciplinary study of the humanities
adjacent to various research fields such as social sciences,
natural sciences, engineering, medicine, and health. If
focusing on humanity sciences, researchers’ primary researchresources have been text media, such as historical records,
literary works, research papers, newspapers, and magazines.
Researchers have engaged in analyses by reading the texts
carefully as part of their research activities. With the spread of
the Internet, various types and large volumes of information
related to area studies are now being distributed on the Web.
This trend has become even more prominent in large-scaledisasters, political conflicts or changes, and national elections.
Even if only limited to text media analysis, there is already a

XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE

Masato Matsuzaki
Faculty of Engineering
Kyoto University
Kyoto, Japanmatsuzaki.masato.86x@st.kyoto-u.ac.jp

massive volume of data on the Web. It has become impossible
for humans to read and analyze all the extracted data. This
paper explores new directions for informatics-based areastudies compatible with big data and the Internet age toaddress these issues.
Our study has used Internet news articles to extract
information related to area studies. The reasons for using
newspapers are that, (1) comparing to social media, such as
Twitter or Facebook, the texts in newspapers are moreconsistent in terms of the use of grammar and vocabulary,which makes it easier to apply natural language processing;(2) newspapers are comprehensive information sources forcollecting information on many topics such as politics,
economics, and culture; and (3) most of the newspaper’s
companies publish news articles not only in paper media but
also as digital data on the Web, which allows for efficient datacollection and processing. Some research in area studies haveused Web text data[1,2], but their purposes are limited to
specific interests; that is, researchers can retrieve necessary
news articles for their analyses using skillfully pre-determined
keywords. This method is helpful for researches with clear andnarrow purposes, such as political changes and regional
disasters. However, when the purpose of area studies is toreconstruct a comprehensive view of an area, such as “Whatkind of area is Taiwan or what will happen to Taiwan in the
future?”, it is impossible to prepare for the right keywords inadvance. Therefore, our previous study [3] developed an
information tool that automatically categorizes news articles
using Topic Models based on the LDA (Latent Dirichlet
Allocation [4]). Experiments and evaluations using Mainichi
Shimbun newspaper articles (CD−Mainichi Shimbun,
published during six years from 2010 to 2015, number ofnewspaper articles: 606,924, number of different terms:
2,683,289, the total number of words: 286,288,248) showed
this method’s effectiveness in categorizing news articles
automatically.
However, the low accuracy of place name extraction fromnews articles and their place identification on maps hinders
the automatic text processing by our tools. The former
problem is related to the choice of the right word for ageographical context. For example, a word “九条 (Kujo or
Kyujo)” appears not only as a geographical reference (e.g., 九
条通: Kujo Street) but also in different contexts such as afamily name (e.g., 九条家:Kujo Family), a crop name (e.g.,
九条葱:Kujo leek), and as a legislative article (e.g., 憲法九条:Article 9 of the Constitution). The latter is the problem of
selecting the correct place from different places with the same
name. For example, there are many places named “ 三 条(Sanjo)” in Japan, such as “三条市(Sanjo City)” in Niigata
prefecture (37.636778, 138.961667), “三条通(Sanjo Street)”in Kyoto City (35.0084, 135.7471 around the midtown of
Kyoto City) and “三条(Sanjo: old town name)” in Nagoya
City (35.107578, 136.902958).
In order to solve the problems cited above, i.e., to extract
place names from texts correctly, we have used BiLSTMCRF[5], combining a Bi-directional Long Short Term
Memory (BiLSTM) network and Conditional Random Fields(CRF). BiLSTM-CRF is known for achieving high precision
in recognition of named entity tasks. Our previous study useda Balanced Corpus of Contemporary Written Japanese(BCCWJ) in which we manually added some tags to indicate
place names as a learning data set to train BiLSTM-CRF. In
this estimation experiment, the BiLSTM-CRF recognition
accuracy was approximately 0.9, which demonstrated theeffectiveness of BiLSTM-CRF. Though our method can
extract place names appropriately from texts, it is not enoughto identify the correct place. Currently, we are trying a simple
strategy: assume that the locations of place names appearing
in the same news article are close. Thus, we compute theweighted average of all candidate coordinates of all place
names in a news article (hereafter, pseudo center). If a placename has some candidate coordinates, from the assumption,
this coordinate should be close to another place name, i.e.,
close to the pseudo center. Thus, we choose the coordinate
with the shortest distance from the pseudo center for eachplace name.
In this paper, Chapter II will describe methods ofpreparing for the news article data, training the LDA topic
model, extracting place names from news articles, andidentifying their locations on maps. Chapter III will show theresults, and lastly, the problems and final considerations will
be discussed in Chapter IV.
II. METHODS
A. Data Collection
As in the previous study, this study also collected news
articles from Mainichi Shimbun (Web aggregation andcommercially available CD-ROM), Asahi Shimbun (Webaggregation), Yomiuri Shimbun (Web aggregation), and AFP(English and Spanish news articles via AFP Forum), andorganizes the following information:
・ ID
・ Article title
・ Article main text
・ Published date/time
・ Article URL (URL for the Web page which includes themain text of the newspaper article)
News articles aggregated from the Web are cleansed,
broken each HTML text into components, and reformatted as
the CSV data following the above structure. The character
encoding for this text file is UTF-8 (without BOM). Since
Mainichi Shinbun news articles in CD-ROM have a key-value

structure, a simple mapping operation can convert this data to
the SCV data.
As a preliminary study, this study used news articles from
Mainichi Shimbun CD-ROM Editions, officially provided by
The Mainichi Newspapers Co., Ltd (national edition and 46
local editions; published ten years from 2010-01-01 to 201912-31) [6].
B. LDA Topic Model
As a method to automatically infer subjects of news
articles, this study uses the LDA topic model. The LDA topic
model expresses a topic as the sets of words that are
statistically likely to co-occur.
The LDA topic model needs Bag-of-Words (BOW) for
training. In BOW, a news article is represented as the set of its
words, disregarding grammar and even word order but
keeping multiplicity. Therefore, we segmented news articles
into words using the KyTea[7], a Japanese morphologicalanalyzer, and secondly, chose nouns, verbs, adjectives,
adjectival nouns, and adverbs that characterize news articles.
We selected the most frequent 100,000 words used only in lessthan 50% of news articles. The statistical of the extracted
corpus is shown in TABLEⅠ.
TABLE I. Corpus specifications of the Mainichi Shimbun CD-ROM

We used the gemsim [8], a python package of the LDA,
for implementation. We set the parameter α as
1.0,√
where N is the number of topics, and i shows the index of the
topic. We used the default parameters for all other hyperparameters.
The LDA topic model uses two parameters, Perplexity and
Coherence, to determine the appropriate number of topics.
Perplexity represents the number of branches or choices. A
smaller value indicates a better model. Coherence is an index
to measure the model’s generalization performance, and alarger value is considered to be indicative of a better learningalgorithm. We trained the LDA topic model by changing topic
numbers from 16 to 256 in 8 increments and calculated
Perplexity and Coherence. At the same time, we interpreted
frequent vocabularies constituting each topic, then finallydetermined the topic number as 184.
C. Named Entity Recognizer
In order to recognize spatial expressions in texts, we
designed a named entity recognizer (NER) for them with ourentity definition.
a) Training Method: We adopted the BiLSTM-CRF
for the task of NER task. We used 5-fold cross-validation forevaluation because the amount of training data is limited. In
each sub-sample, we divide news articles into train:dev:test =3:1:1, train model on training data, and then choose the epoch
achieving the best F-measure for development data. A modelwith the best F-measure over all sub-samples is used for the
final model as input for the longitude-latitude estimator.
b) Training Data: We selected 100 news articles eachabout earthquakes and floods and annotated them. We listed
the target news articles by choosing news articles with thehighest probabilities for the chosen topic. Note that weselected news articles over 4,096 bytes.
c) Annotation Standard: We categorize spatial
expressions into two types: absolute expression and relative
expression. Previous entity tagging schemes regarding
location [9,10] focus on absolute expression, and relative
expression is overlooked. Relative expressions are valuable
for inferring the author’s intent regarding location; therefore,
we establish an annotation standard for both absolute andrelative spatial expressions. We establish five absolute
expression tags.
L: Locations and facilities (e.g., Olympic Games were heldin Tokyo).
LD: Locations in disaster names (e.g., Great East Japan
Earthquake).
LF: Locations included in a word that is not a spatial
expression (e.g., New Yorkers).
LO: Locations meaning organizations but impossible todetermine by context.
LV: It may indicate a location, but the context is ambiguous
and cannot be determined. Except for LO.
We also establish five relative expression tags.
RLI: Inside of location (e.g., northern Kyoto).
RLO: Outside of location (e.g., about 450km northwest of
Okinawa Island).
RLA: Neighborhood of location (e.g., around Kyoto).
RLB: Between locations (e.g., between Tokyo and Osaka).
RL*: Expressions other than those above.
D. Longitude-Latitude Estimator
The longitude-latitude estimator is a module to estimate anabsolute place for each NE (named entity). In this study, theabsolute value is a pair of longitude and latitude, which allows
us to point the NE to a single point on the globe’s surface.
The estimation process is composed of two stages. The
first step processes the division of compound location entities
into individual expressions. For example, “神奈川・千葉両
県 (both Kanagawa and Chiba prefecture)” is divided into “神奈 川 県 (Kanagawa prefecture)” and “ 千 葉 県 (Chiba
prefecture).” Conversion rules generated by observation fromthe news articles do this. Then, we search geo-coordinates in
the gazetteer database by entity names. The gazetteer database
contains locations and positions collected from the Historical
Gazetteer Data[11] and Wikidata[12]. It also contains theannotated geolocation data we used to expand the learning
data set of BiLSTM-CRF, where we added the correct position(longitude and latitude) to the tags used to indicate a place
name. It is geocoded using Wikidata, OpenStreetMap[13], andCommunity Geocoder[14].
The search considers both the exact match and the partial
match. If we find multiple candidates for an entity, we scorethem and choose the best one. I.e., we compute the average ofcandidate coordinates for each entity and then the average of
all entities (pseudo center). Then, we measure the distance
between the pseudo center and candidate coordinates for each
entity and add a penalty if a candidate is found by partialmatch. Finally, we choose the candidate with the shortest
distance.
In estimation, we give the output from NER for an entire
news article to the estimator. The pseudo center is computedby using all candidate coordinates in the news article and used
globally.
III. RESULTS
A. LDA Topic Model
This section will try to interpret the results of the LDA
topic model, focusing on the first one-year transition of topics
related to the 東日本大震災 (2011.3.11 Great East Japan
Earthquake. Hereafter the Earthquake). As the topics seemed
to be related to the Earthquake, we chose three topics; 37, 110,
and 124. The frequent top five words assigned to each topic
are as follows:・ Topic 37: 災害/名詞, 避難/名詞, 防災/名詞, 地震/名詞, 被害/名詞 (disaster/noun, evacuation/noun,
disaster prevention/noun, earthquake/noun,
damage/noun)
・ Topic 110: 原発/名詞, 電力/名詞, 発電/名詞, 原子/
名詞, 放射/名詞 (nuclear power/noun, electric
power/noun, power generation/noun, atom/noun,
radiation/noun)
・ Topic 124: 震災/名詞, 被災/名詞, 地/名詞, 日本/名詞, 復興/名詞 (earthquake/Noun, damage/Noun,place/Noun, Japan/Noun, reconstruction/Noun)
We interpreted Topic 37 as the occurrence of disasters,
associated damages, evacuations, and the like. Likewise,
Topic 110 as nuclear accidents, associated power losses,
radiation leaks, and the like. Topic 124 is similar to Topic 37,
but we interpreted it as having implications for reconstruction
and assistance. Fig.1 shows how these three topics’
composition changed in a year after the Earthquake acrossseveral regions. Each topic value results from the MovingAverage calculation whose time-period is seven days; each
topic value is obtained by first accumulating the topic ratio of
all news articles published during the time-period andsecondarily dividing the accumulation by the number of news
articles.
We can see from this figure that the damage situations in
the disaster areas and their surrounding areas were different,or even in the disaster areas, the situations were also quite
different between the places where the tsunami damages were
dominant and the places where nuclear accidents were
dominant.
In the case of the national edition of Mainichi Shimbun,
the ratios of all three topics rose sharply immediately after the
Earthquake and then gradually declined. Not shown in thisfigure, these topic ratios continued to decrease except a slightincrease every March, the month of reference for when the
Earthquake occurred. After ten years, it returned to the samelevel before the Earthquake.

Fig. 1 Change of three topics in one year across different areas. (upper left) news articles from the national editions. (upper right) regional editions of Tokyo.
(lower left) regional editions of Fukushima prefecture, which was suffered from the atomic reactor accidents. (lower right) regional editions of Miyagi prefecture,
which is one of the most damaged areas by the Earthquake and Tsunami. (Blue-lines) topic 37; disaster, evacuation, disaster prevention, earthquake, damage,etc. (Orange lines) topic 110; nuclear power, electric power, power generation, atom, radiation. (Green lines) topic 124; earthquake, damage, place, Japan,
reconstruction etc.

The ratios of the three topics in the Tokyo edition are
similar to those in the national edition. However, the ratio of
the Topic 110 on nuclear reactors and radiation is slightly
superior. Tokyo area was also affected by the Earthquake, but
to a lesser extent than in the Earthquake-stricken areas.
However, the spread of radioactive contamination due to the
nuclear accident became a big issue. Actually, at some of thepeaks seen in Topic 110, there were news articles about
implementing independent radiation dose measurements by
local governments, rapid increases in dose in some cities, orfinding radiation hotspots.
The decreasing trend of the three topic ratios in the Miyagi
and Fukushima editions was slight, unlike the national and
TABLE Ⅱ. Result of NER performance on test data. Answer and Result are
the number of occurrences on test data and predicted data, respectively.
True is the number of successful predictions. Result of tag LV, LF, and RL*
is not shown because they have not occurred in test data.

Tokyo editions. The reason may be that the Earthquake
severely damaged both areas more than Tokyo.
On the other hand, the trends in topic ratios in bothprefectures are pretty different, which may reflect that thetsunami heavily damaged Miyagi prefecture, and the numberof fatalities and missing persons was higher than that of
Fukushima prefecture. At the same time, Fukushima
prefecture was greatly affected by the nuclear accident.
Therefore, there are many news articles about earthquake
damages and reconstructions in the Miyagi edition, and thistendency has continued for several years. However, there area few news articles about nuclear accidents and radiation. On
the other hand, in the Fukushima edition, Topic 110 related to
the nuclear accidents was outstanding, and the number ofrelated news articles increased two years after the Earthquake.
B. Named Entity Recognizer
As parameters, we used: a word embedding size of 300
and used all occurrence words for embedding; a hidden layer
size of 256 with a batch size of 30; and a learning rate of 1e-3
with a weight decay of 1e-6 for the SGD (stochastic gradientdescent) optimizer. We trained the model up to 100 epochs oneach sub-samples and choose the best epoch using the Fmeasure of development data and use it for prediction. We
evaluate precision, recall, and the F-measure by using test dataof all sub-samples.
The performance result is shown in TABLE Ⅱ, and theconfusion matrix of tags is shown in Fig.2. It is the best Fmeasure in tag sets for the tag LD because disaster names are組み/O で/O は/O ... (Himeji City’s latest project is ...)” istagged as “今回/O の/O 姫路/L-B 市/L-I の/O 取り組み/Oで/O は/O ....” For the RLO tag, the RLO tag following the L
tag tends to be predicted as the L tag. For example, “ＪＲ/LB 豊 岡 /L-I 駅 /L-I 前 /RLO-B (in front of JR Toyooka
Station)” is tagged as “ＪＲ/L-B 豊岡/L-I 駅/L-I 前/L-I.”

Fig.2. Confusion matrix of tags. The confusion matrix shows the relationshipbetween the true tag and the predicted tag that is the result of recognition.
Each row represents prediction occurrences for the true tags. In an ideal
situation, only the diagonal component of a matrix would be non-zero,otherwise zero. The more non-zero there are other than diagonals, the more
likely the tag is to be mispredicted. In addition, the confusion matrix shows
how the recognizer tends to tag incorrectly. For example, there is a large value(filled with darker blue) in the off-diagonal L tag column for the LO tag,indicating that the LO tag tends to be incorrectly predicted as the L tag.

determined by the government and local authorities and haveexpressions with less variation. In contrast, the F-measure of
the LO and the RLO tag is very low. For the LO tag,administrative locations are predicted as L tag in many cases.
For example, “今回/O の/O 姫路/LO-B 市/LO-I の/O 取り

C. Longitude-Latitude Estimator
We randomly choose 50 news articles from 873 news
articles that include topic numbers 37, 110, or 124 in the
Miyagi and Fukushima editions. The news articles are notused for NER training and evaluation. We computed theaccuracy of coordinate estimation for successfully tagged
entities in the NER stage, and the result was 71.43%.
Successfully estimated coordinates come from Wikidata andthe annotated geolocation data. In some cases, different
coordinates are estimated for the same location. For example,“福島” (Fukushima) does not refer only to one place because
the name is also used in other places outside Fukushima
prefecture, such as Osaka. Such coordinates can affect thepseudo center and result in instability and performance drop.
Fig.3 shows the geographical distribution of news articles
estimated by the above method. The upper row shows the
topic distribution of Fukushima prefecture, which wasaffected by the nuclear accident, and the lower lowes showsthat of Miyagi prefecture, which suffered tsunami damages.
The left column shows the distribution in March 2011, justafter the Earthquake happened, the middle column shows the
distribution in August 2012, almost one year and a half after
the Earthquake.

Fig. 3 Spatial distribution changes of three topics in Fukushima and Miyagi prefecture. (upper left) Fukushima prefecture in March 2011, just after the
Earthquake, (upper middle) Fukushima prefecture in March 2012, one year after the Earthquake. (upper right) Fukushima prefecture in August 2013. (lowerleft) Miyagi prefecture in March 2011, just after the Earthquake, (lower middle) Miyagi prefecture in March 2012, one year after the Earthquake. (Blue dots)
topic 37; disaster, evacuation, disaster prevention, earthquake, damage, etc. (Red dots) topic 110; nuclear power, electric power, power generation, atom,
radiation. (Green dots) topic 124; earthquake, damage, place, Japan, reconstruction etc.

In Fukushima prefecture, Topic 37 (related to disasters)
points were decreased and distributed in specific areas; on theother hand, Topic 124 (related to reconstruction) points
increased. In Miyagi prefecture, although the distribution of
Topic 37 points increased like Fukushima prefecture, Topic
24 points were still distributed to many places, mainly incoastal areas. Above right is the topic distribution of
Fukushima in August 2013, more than two years after the
Earthquake. The upper right is the topic distribution of
Fukushima in August 2013, more than two years after the
Earthquake. As mentioned in Ⅲ a), the ratio of Topic 110
increased from this time. Although detailed investigation will
be needed, it is presumed that the Miyagi edition began
publishing radiation dose measurements of some specificareas regularly, which increased the number of news articles
related to Topic 110.
IV. CONCLUDING CONSIDERATIONS
The original motivation of this research was to look for
new methods of area studies within the domain of informatics
by using advanced information methods. Therefore, we
addressed the issues of (1) targeting a large amount of datathat researchers cannot read and analyze manually and (2)establishing a method for comprehensively grasping an area
beyond a specific viewpoint. As the first step, we have beendeveloping a method to classify a large amount of text data
based on topic (or subject), geographical position, and time.
Regarding the classification of text by topics, our previous
study verified the effectiveness of LDA.
This study tried to visualize the distributions of topics ona map. We demonstrated that BiLSTM-CRF could predict
named entities regarding location with approximately 0.8 Fmeasure for named entity recognition. We also reported thetrend of error prediction and tag ambiguity. For longitudelatitude estimation, we demonstrated that a simple strategy
that uses a gazetteer database could achieve approximately
70% accuracy and explored the limitations of this strategy. We
expect that future work will improve tag disambiguation,
make the coordinates selection method more robust, andexplore the usefulness of relative location expression to
improve geocoding and visualization.
We have promoted the modularization of methods forcollecting news articles from the Web, cleansing messy text
data, recognizing and extracting tokens including namedentities for further analysis, topic analysis, and visualizing
topics on a map, which have been realized in the research sofar. We are building an easy-to-use analysis tool by puttingthese modules together.
Time as much as location is essential information for areastudies but has not been covered in our studies. Our researchsources have been news articles, and we could use thepublication date (though this premise is not always applicable
to all news articles). However, as for general text data, it isnecessary to extract time-related words and convert them to a
standard format such as ISO 8601. We are considering thepossibility of applying BiLSTM-CRF to predict time-related
words. In addition, a tool equivalent to gazetteer databases thatorganize place names and their latitudes and longitudes has
already been developed. Supposing that we could process
time information using these methods, in that case, we believethat our first object, which is “developing a method to classify
a large amount of text data based on topic, geographicalposition, and time” will be achieved.
ACKNOWLEDGMENT
This study received support from the JSPS KAKENHI
Grant Number JP21H04376, Kyoto University Research
Coordination Alliance Research Unit, and the Center forSoutheast Asian Studies Kyoto University Glocal Information
Network.
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