Enriching Travel Guidebooks with Travel Blog Entries
and Archives of Answered Questions
Kazuki Fujiia,
Hidetsugu Nanbaa,
Toshiyuki Takezawaa, and
Aya Ishinob,ab

Graduate School of Information Sciences, Hiroshima City University
Department of Information Systems in Business, Hiroshima University of
Economicsa) {fujii, nanba, takezawa}@hiroshima-cu.ac.jp, b) ay-ishino@hue.ac.jp
ay-ishino@hue.ac.jp

Abstract
Travellers planning to visit particular tourist spots need information about their destination andthey often use travel guidebooks to collect this information. However, guidebooks lack specificinformation, such as first-hand accounts by users who have visited the specific destination. To
compensate for the lack of such information, we focused on travel blog entries and archives ofanswered questions. In this paper, we propose a method for enriching guidebooks by matching
and aligning the information with blog entries and questions answered (QA) archives. This is athree-step method. In Step 1, we classify pages of guidebooks, blog entries, and QA archives
into five types of content, such as “watch,” “dine,” etc. In Step 2, we align each blog entry and
QA archive with guidebooks by taking these content types into account. In Step 3, we match
each blog entry and QA archive with individual pages in guidebooks. We conducted someexperiments, and confirmed the effectiveness of our method.
Keywords: travel guidebook; blog; QA archive.

1 Introduction
Travellers planning to visit particular tourist spots need information about these
tourist destinations, and they often use travel guidebooks to collect this information.
Guidebooks give basic information about famous tourist spots, souvenir shops, andrestaurants. As another method to collect the information, there are portal sites thatare operated by travel companies and local governments for the benefit of the tourist.
However, as there are also sites that are not updated frequently and there are large
differences in the amount of information on each tourist destination, many travellers
use guidebooks to get basic information.
Travellers who read guidebooks only get basic information from them and cannot
determine how to travel between the destinations listed in the guidebooks or at whichhotel to stay with their family. To help travellers read guidebooks, we focused on
travel blog entries and archives of questions answered (QA), both of which containvaluable information, such as first-hand accounts by users who have visited theparticular destination. In addition, blog entries and QA archives have more descriptive
content than other SNSs, such as twitter or various review sites. Therefore, blog
entries and QA archives are considered useful information sources for obtaining travel
information.

In this paper, we propose a method for enriching guidebooks by matching themagainst blog entries and QA archives. In addition, we constructed a prototype systemthat enables guidebook enrichment. By using our system, users can obtain basic
information from guidebooks and valuable information based on the personal
experiences of travellers from blog entries and QA archives.
The remainder of this paper is organized as follows. Section 2 shows the systembehaviour in terms of snapshots. Section 3 discusses related work. Section 4 describes
our method. To investigate the effectiveness of our method, we conducted someexperiments, and the experimental results are reported in Section 5. We present someconclusions in Section 6.

2 System Behaviour
In this section, we describe our prototype system, which can provide enrichedguidebooks. For scanned and OCR-processed guidebooks, our system matches blog
entries and QA archives to each guidebook page automatically.
Kakeromajima
IslandAccommodation(1) Blog (2) QA
Fig.1. An example of a page of an enriched guidebook. This page gives details about
Kakeromajima Island in Japan.
Figure 1 shows an example of a page of an enriched guidebook; it gives details about
tourist spots and accommodation facilities on Kakeromajima Island. In this figure,when a user clicks the “Blog” button (1), a list of blog entries related to the guidebook
page is shown. In the same way, when a user clicks the “QA” button (2), all QA
archives related to the guidebook page are shown. Figure 2 is an example of a QA
archive that was aligned with the guidebook page shown in Figure 1. In this archive,
the questioner asked recommendation of guesthouses in Kakeromajima Island. For
this question, the answerer recommended guesthouses in Ukejima Island near to
Kakeromajima. Users of our system can know basic information from guidebooks,while additional valuable information from blog entries and QA archives, which wereautomatically aligned by our system.
There are several travel portals, such as “Rakuten Travel (http://travel.rakuten.co.jp/)”and restaurant review sites, such as “Tabelog (http://tabelog.com/).” Although thesesites provide customer reviews, we focus on blog entries and QA archives, because
they contain a lot of valuable information such as follows.
• Local information provided by local people, most of which are not written in
guidebooks.
• Tourist spots or events influenced by transportation. (e.g. “I don’t recommend toelderly people, because we need to go up and down a hill a couple of times.”)• Unrecommended information, such as shown in Figure 2.

Question:
Can you please recommend any
guesthouses in Kakeromajima Islandin Amami-Oshima? I'm not reallyinterested in diving so guesthouses
near the shore is not really required
and I will visit in late July.
Answer:Guesthouses? I really don't
recommend you to stay in Kakeromajima, but I recommend those in Ukejima. Ukejima is just next toKakeromajima and the guesthouses
there are better because...

Fig. 2. An example of an automatically aligned QA archive for the guidebook pageshown in Figure 1.

3 Related WorkIn this section, we introduce some studies on enriching texts. Textbooks used indeveloping regions are largely text-oriented and lack good visual materials. Therefore,
Rakesh et al. (2011) proposed a method that enhances the quality of textbooks byassigning images in Wikipedia to each section in textbooks. Nie et al. (2013)
proposed a method that is able to enrich textual answers in QA archives. For somequestions, such as “What are the steps to make a weather vane,” textual answers may
not provide sufficient natural and easy-to-grasp information. Therefore, they
automatically provide accompanying videos and images that visually demonstrate theprocess of the objects. Lu et al. (2009) proposed a method to visualize textual travel
blog with images. Bressan et al. (2008) proposed a travel blog assistant system thatfacilitated the travel blog writing by selecting for each blog paragraph the most
relevant images from an image set.
All these works focused on enriching texts by multimedia data for supporting tounderstand the texts, while we focus on enriching multimedia data (guidebooks

including both texts and images) with other textual data, such as QA archives andblogs. Especially significant characteristic of our work is that our target data are
printed guidebooks, because our method is easily applicable to any printed materials,
and has a potentiality to enrich them.

4 Enriching Guidebooks with Blog Entries and QA Archives
We propose a method for enriching guidebooks by aligning the content with blog
entries and QA archives. In this section, we describe our method for implementing
this alignment. The procedure of our method consists of the following three steps.
Step 1: Content-type (see Table 1) classification of guidebook pages, blog entries, and
QA archives. One guidebook page is aligned with blog entries and QA archives,
which have the same content type as the guidebook page. The results of the contenttype classification are used in Step 3.
Step 2: Alignment of blog entries and QA archives with a guidebook.1
Step 3: Alignment of blog entries and QA archives with a guidebook page, having thesame content-type, which was determined in Step 1. The results of Step 3 are the final
output for enriched guidebooks.
These steps are described in detail in Sections 4.1, 4.2, and 4.3, respectively.
4.1 Content-Type-Classification of Guidebook Pages, Blog Entries, and QA
Archives
4.1.1
Definition of Content Types
Generally, each guidebook page contains travel information in several content types,
such as sightseeing spots, souvenir shops, or accommodation. Each guidebook pagecan be classified into one of the six content types that are typical for tourism (Table 1).
Table 1. Content types and their descriptions
Content Type

Criterion

Watch
Experience
Buy
Dine
Stay
Other

Page about sightseeing for watching enjoyment
Page about experience (scuba diving, dance)
Page about shopping or souvenir stores
Page about drinking and dining
Page about accommodation
None of the above applies to this page

It is possible to supplement a guidebook page with more relevant information byaligning blog entries and QA archives that have the same content type as theguidebook page.
The information published in guidebook pages was mainly content of type “watch,”“experience,” “buy,” “dine,” and “stay.” We defined guidebook pages that did notbelong to these content types as content type “other.” Guidebook pages that were
judged to belong to “other” contain a lot of information, not all of which is directly
1

We employed a coarse-to-fine alignment, that is to align in a coarse (guidebook) level (Step 2),
then in a fine (page) level (Step 3).

related to tourist destinations, for example, advertisement pages, transportation
information for arriving at the tourist destination, and also how to get a passport.
Therefore, we align blog entries and QA archives that have the same content type as
the guidebook pages with the guidebook pages that were classified into “watch,”“experience,” “buy,” “dine,” or “stay.” We classify guidebook pages, blog entries,
and QA archives into each content type listed in Table 1, other than content type“other,” to take advantage of alignment. Guidebook pages of each content type havealready been published; however, it is not realistic to classify them manually because
guidebooks are updated on a regular basis. Hence, guidebook pages were also
subjected to automatic content-type classification. A guidebook page was classified
into “watch” and “buy” if it contained information about “watch” and “buy.” Blog
entries and QA archives were also treated similarly. It is considered that by
preforming such a content-type classification, we can align blog entries and QA
archives of appropriate content type even if the guidebook page was classified into
several types.
For content-type classification, we employed a machine-learning technique using text
information and image information; these are explained in Sections 4.1.2 and 4.1.3,
respectively.
4.1.2
Content-Type-Classification using Text Information
In a guidebook page belonging to “watch,” words such as “display” and “museum”
appeared frequently. A page judged as belonging to “experience” contained words
such as “instructor” and “beginner.” Namely, the guidebook page, which wasclassified into each content type, tended to contain words specific to the content type.
Moreover, words in guidebooks that were not judged as “watch,” “experience,” “buy,”“dine,” or “stay,” were used as the words specific to the content type “other.”
Therefore, we collected words that were peculiar to each content type for machinelearning, and employed information gain (IG)2 as the feature selection method.
Our method computed the value of the words in each content type by the IG. The
words used were nouns, verbs, and adjectives. In addition, out of these words, weremoved stop words longer than 15 letters, shorter than 2 letters, alphanumericcharacters, and words with appearance frequency of less than two. We used MeCab(http://mecab.sourceforge.net/) as a Japanese, morphological analysis tool to identify
the part of speech. We collected cue words that have a value over the threshold of theIG. The threshold value was determined by the preliminary experiment. In the sameway, we collected cue words from blog entries and QA archives.
4.1.3
Content-Type-Classification Using Image Information
Guidebooks contain many images. For example, pages judged to belong to contenttype “watch” may include many images of mountains or seashores. Pages judged as
belonging to content-type “dine” may include many images of various foods. For thisreason, we assumed that images were good clues for classifying the content type by
analysing what the images contain. Therefore, for the content-type classification ofpages of guidebooks, we also employed image information as features for machine2

For example, if we collect words that are peculiar to “watch” type, we compare words in type“watch” blog entries with other ones.

learning. We used one guidebook page as one image. We adopted the Bag of VisualWords (BoVW) (Csurka et al., 2004) for image information. BoVW represents
images by vectors of the frequency of appearance of local features; it is also a popularmethod for the task of object recognition. BoVW was applied Bag of Words, which
represents documents by vectors of appearance frequency of words in natural
language processing to images.
First, we extracted local features from the training image data by dense sampling.
Second, we extracted local features and performed clustering to create a
representative vector (visual word). We opted for k-means as the method forclustering feature vectors of images. The many feature vectors were clustered into
1,000 visual words by k-means (1,000 visual words were created in a preliminary
experiment). Finally, we generated BoVW by histogram by counting the number ofoccurrences of visual words as an approximation of guidebook pages. BoVW features
were generated for each page. The generated features were used for machine learning.
Blog entries also include many images, which are important clues. However, we
could not collect images in blog entries in Yahoo! Blog (http://blogs.yahoo.co.jp/),because images are embedded by JavaScript, and we were not allowed to crawl fromthe blog server. For this reason, the type classification in blog entries uses only text
information.
4.2 Alignment of Blog Entries and QA Archives with Guidebooks
In this section, we first explain why we employed a coarse-to-fine alignment. Let us
consider aligning blog entries and QA archives about Miyajima Island with thecorresponding page in a guidebook of Hiroshima. The island is located in Hiroshima,
but the page does not necessarily include the word “Hiroshima” despite the fact thateach page contains information about Hiroshima. In this case, it is difficult to alignblog entries and QA archives that are relevant to information for the guidebook’s page
contents and for Miyajima. Therefore, it is necessary to take account of both global
and local contexts of a guidebook for more accurate alignment of a guidebook page
with blog entries and QA archives. There is a related work focusing on global andlocal contexts of a document. He et al. (2010) removed cited documents from anarticle, and estimated cited documents from text surrounding the citation or theplaceholder. They considered that some parts of the document such as the title andabstract have a global context and allow words related to the article to be extracted.
The text surrounding a citation or placeholder comprises local context and allows
characteristic words related to the citation to be extracted. They enhanced theprecision of estimation of the bibliography by using extracted words. We can consider
that our framework is similar to their framework by regarding the local context as one
page of the guidebooks. In particular, we consider global context as the words related
to the guidebook such as “Hiroshima,” and local context as the words that appear on
the guidebook page. Therefore, we first align blog entries and QA archives with theguidebook to improve the precision of alignment, and then align with the guidebook
page.
The remainder of this section explains how to align blog entries and QA archives witha guidebook. As the location names of tourist spots frequently appear in blog entries,
QA archives, and guidebooks, the appearance of the location name is important foralignment. We align blog entries and QA archives with guidebooks by using theappearance frequency of the location name. To extract location names fromguidebooks,
blog
entries,and
QAarchives,
weused
CaboCha(http://code.google.com/cabocha/) as the Japanese syntactic parser.
We used the results of content-type classification in Step 1 to align blog entries and
QA archives with the guidebook page. Here, we explain why the results of contenttype classification are necessary. Generally, it is known that context has typicalcontent-type constituents. For example, academic articles have typical constituents,
namely “background,” “purpose,” “method,” “conclusion,” or “consideration.” Kando(1997) reported on a method that creates an index by using only sentences that have aparticular type and found that it is more accurate than the method that uses academic
article detail in the task of academic articles retrieval. The constituents of guidebooks
are the content types shown in Table 1. We attempted to achieve a more accuratealignment by using the results of content-type classification.
Guidebooks
pages

experience

dine

Blog
entriesexperience
experience

List oflocation
nameList oflocation
nameexperiencebuy

Fig. 3. Alignment of blog entries with guidebook pages using content-typeclassification
Figure 3 shows how to use the results of content-type classification for aligning blog
entries and QA archives with guidebooks. When we selected the guidebooks that werealigned with blog entries classified into “experience,” we used location names thatwere extracted from the blog entries and the guidebook page having the same contenttype, “experience.” A similar flow of operations was performed in the case of othercontent types: “buy,” “watch,” “dine,” and “stay.” Using extracted location names foraligning blog entries and QA archives with the guidebook page, we proposed two
methods, namely the k-Nearest Neighbour (KNN) method and the Support Vector
Machines (SVM) method. In the KNN method, we used the SMART measure to
calculate the similarity between guidebooks and blog entries or QA archives. Blog
entries and QA archives can be aligned with the guidebook page when the similarity
is higher than the threshold. The threshold was determined to be the value when therecall was above 15% and the precision was the highest in training data by thetwofold cross-validation test. In the SVM method, we constructed models for eachguidebook. Models classify whether intended blog entries and QA archives can bealigned with guidebooks.
4.3 Alignment of Blog Entries and QA Archives with a Guidebook Page
We extracted location names from guidebooks, blog entries, and QA archives, andthen calculated the similarity between them for the same content type and alignedwith the guidebook page having the highest similarity. The similarity calculation uses
the cosine degree. We used the tf*idf for term weighting. The idf scores were
calculated by using the number of hits in the web search engine.

5 Experiments
We conducted three experiments: (1) content-type classification of pages ofguidebooks, blog entries and QA archives, (2) alignment of blog entries, and QA
archives with guidebooks, and (3) alignment of blog entries and QA archives withpages of guidebooks. We report on these in Sections 5.1, 5.2, and 5.3, respectively.
5.1 Content-Type-Classification of Guidebook Pages, Blog Entries, and QA
Archives
Datasets and Experimental Setting
We used 2,897 pages of 20 guidebooks, 1,000 blog entries that were collected by themethod of Nanba et al. (2009), and 9,388 QA archives registered in the “region /travel” category of Yahoo! Answers. Then we classified them manually, and usedthem for our examination. It is possible to classify a guidebook page, blog entry, and
QA archive into more than one content type. Table 2 shows the number of eachcontent type. We performed a twofold cross-validation test. We used TinySVM(http://chasen.org/~taku/software/TinySVM/) software as the machine-learning
package, and precision and recall as evaluation measures. Here, precision is thefraction of classified instances that are relevant, and precision is the fraction of
relevant instances that are classified. We considered that precision is more important
than recall because there are a large number of blog entries and QA archives on theweb, and low recall would not become a serious matter in our case. In addition,
misclassification of content-type causes wrong alignment of blog entries and QA
archives.
Table 2. The number of pages of guidebooks, blog entries, and QA archives for eachcontent-typeContent Type
Watch
Experience
Buy
Dine
Stay
Other

Guidebook
102
78
418
741
278
975

Blog
395
241
163
382
134
193

QA Archive
620
412
191
502
257
7888

Experimental Methods
To investigate the effectiveness of our method, we classified content types using thefollowing four methods.

l The IG method: Used cue words collected by IG as features for machine learning.
l The IG+BoVW method: Used cue words collected by IG and BoVW as features
for machine learning for guidebooks.
l BoVW method: Used BoVW as features for machine learning.
l Baseline method: Used all words that appeared in datasets.
Table 3. Evaluation results for content-type classification of pages of guidebooks
FeatureWord(Baseline)
IG
IG+BoVW
BoVW

MeasurePrecision
RecallPrecision
RecallPrecision
RecallPrecision
Recall

Watch
46.0
53.6
73.3
32.1
74.1
37.3
61.9
14.7

Experience
16.9
15.4
91.7
17.0
91.7
17.0
―
―

Buy
25.1
23.3
81.5
20.0
76.2
28.7
―
―

Dine
41.7
49.2
80.5
32.4
77.6
35.3
72.0
5.6

Stay
39.1
17.0
74.0
32.8
75.4
36.8
―
―

Average
46.9
30.9
75.6
27.9
75.8
33.7
―
―

Table 4. Evaluation results for content-type classification of blog entries
FeatureWord(Baseline)IG

MeasurePrecision
RecallPrecision
Recall

Watch
68.4
60.6
66.7
64.0

Experience
55.3
37.0
60.2
33.7

Buy
50.7
20.8
54.9
31.8

Dine
75.1
67.4
77.2
69.9

Stay
49.8
19.2
58.9
34.3

Average
66.4
48.7
65.9
51.0

Table 5. Evaluation results for content-type classification of QA archives
FeatureWord(Baseline)IG

MeasurePrecision
RecallPrecision
Recall

Watch
72.7
70.6
71.1
44.4

Experience
56.6
43.3
81.4
11.9

Buy
77.5
41.7
90.1
33.3

Dine
72.2
69.6
71.8
47.5

Stay
82.0
65.4
90.8
20.4

Average
70.9
61.1
80.7
32.4

Results and Discussion
The evaluation results for the content-type classification of pages of guidebooks, blog
entries, and QA archives are shown in Tables 3, 4, and 5. In Table 3, “-” indicates that
we could not conduct machine learning because of the lack of training data. As shownin Tables 3, 4, and 5, the IG method obtained high precision in each dataset. However,
the average precision score of blog entries was smaller than that of guidebooks and
QA archives. Blog entries use informal expressions more frequently than guidebooks
and QA archives. Therefore, we considered that the method based on cue words could
not obtain high precision. To investigate the validity of this guess, we calculated theunique word ratio in guidebooks, blog entries, and QA archives, using the following.
Unique word Ratio =the number of unique words in the content
the total number of words in the content

The unique word ratio of guidebooks, blog entries, and QA archives was 0.082, 0.112,
and 0.078, respectively. The unique word ratio of blog entries was higher than thatunique word ratio of guidebooks or QA archives. It is considered that words that
seldom appear in training data appear frequently in test data, if the unique word ratio
is high. In addition, the number of blog entries that were classified manually into “buy”
and “stay” is too small to train. That is, the IG method cannot cyclopedically collectcue words, and improve the precision of content-type classification in blog entries.

5.2 Alignment of Blog Entries and QA Archives with Guidebooks
Datasets and Experimental Setting
We used 90 guidebooks, 1,000 blog entries, and 1,998 QA archives. A guidebook wasaligned manually with blog entries and QA archives that were thought to be related to
the guidebook. We used precision and recall as evaluation measures.
Experimental Methods
To investigate the effectiveness of our methods, we conducted tests using thefollowing. The KNN_TYPE method was only used for results of content-typeclassification. This experiment used results of the content type that were obtained by
the IG+BoVW method for the guidebook page and the IG method for blog entries and
QA archives. Except for the KNN_TYPE method, the methods did not use the resultsof content-type classification.
l KNN_TYPE: Calculated the similarity by KNN using the appearance frequencyof location names. Location names were extracted from datasets of travel
guidebooks, travel blog entries, and QA archives having the same content type.
l

KNN_LOC: Calculated the similarity by KNN using the appearance frequencyof location names. Location names were extracted from the datasets regardlessof the content type.

l

BASE_KNN: Calculated the similarity by KNN using the appearance frequency
of nouns extracted from the datasets.

l

BASE_SVM: Used SVM. Features were the appearance frequency of nouns
extracted from the datasets.

Results and Discussion
The evaluation results are shown in Tables 7 and 8. As can be seen from these tables,
the KNN methods were more useful than the SVM methods. The KNN_TYPE
method obtained the best performance. It had high precision but low recall. However,the low recall was not a serious matter in this case, because the KNN_TYPE method
could align 99 blog entries and 1,561 QA archives for a guidebook page, when
applying our method to real datasets. We considered that precision was more
important than recall, because enriching guidebooks having appropriate information
was more beneficial than enriching guidebooks having incorrect information. Our
methods allow the flexible alignment of appropriate information with guidebooks byusing location names and results of content-type classification. Therefore, it ispossible to align with blog entries and QA archives, which have various information
if the recall will low.
The main cause of the failure was that location names, which were not the destinationof the trip, were extracted from blog entries and QA archives. This was because of theuse of CaboCha, which is the Japanese syntactic parser, for extracting location names
from datasets. These web contents contain location names before moving, such as thetraveller’s home.
Table 7. Evaluation results for an automatically aligned blog entry for a guidebook
Precision
Recall
Our methods
Baselinemethods

KNN_TYPEKNN_LOCBASE_KNN
BASE_SVM

81.1
76.7
27.3
21.6

20.4
20.1
15.5
3.0

Table 8. Evaluation results for an automatically aligned QA archive for a guidebook
Precision
Recall
Our methods
Baselinemethods

KNN_TYPEKNN_LOCBASE_KNN
BASE_SVM

85.8
78.3
48.7
40.5

21.0
20.6
16.6
18.4

5.3 Alignment of Blog Entries and QA Archives with a Guidebook Page
Datasets and Experimental Setting
We performed experiments that aligned 100 blog entries and 100 QA archives with 90
guidebooks.
Experimental Methods
To investigate the effectiveness of our method, we conducted tests using thefollowing methods. Our methods selected guidebooks that were aligned with blog
entries and QA archives by Step 2. In this experiment, we used guidebooks that werealigned manually with blog entries and QA archives by the KNN_TYPE method ofSection 5.2.
l
Our method 1 (coarse-to-fine, without content type): We selected guidebooks
that were aligned with blog entries and QA archives by the KNN_TYPE
method described in Section 5.2. We calculated cosine similarity between
selected guidebook pages and blog entries or QA archives, and aligned themwith the guidebook page having the highest similarity.
l

Our method 2 (coarse-to-fine, with content type): We selected guidebooks thatwere aligned with blog entries and QA archives by the KNN_TYPE method.
We calculated cosine similarity between the selected guidebook pages and blog
entries of the same content type as the guidebook pages or QA archives, andaligned them with the guidebook page having the highest similarity.

lBaseline method (not coarse-to-fine, without content type): Calculated thecosine similarity between the guidebook pages and blog entries or QA archives.
Blog entries and QA archives were aligned with the guidebook page having thehighest similarity.
Evaluation Methods
We used a questionnaire survey as the evaluation method. The questionnaire contents
asked whether blog entries and QA archives that were aligned with the guidebook
page were “Appropriate” or not for travellers collecting information. Each guidebook
page was judged by 11 judges. The evaluation results for each guidebook page were
decided by a majority vote.
Results and Discussion
The questionnaire results are shown in Table 9, and indicate that our method could
appropriately align blog entries and QA archives with guidebook pages. For the
results of blog entries, our methods 1 and 2 obtained better results than the baseline
method. Therefore, our method that determines a guidebook-related blog entry first
and then aligns the blog entry with the guidebook page could align more appropriately
than the baseline method that aligns a blog entry with a guidebook page at one time.
There was not much difference between our methods 1 and 2 because most blog

entries had been written about lots of things and had been classified into multiple
content types.
In the results for QA archives, our method 2 gave the best performance. QA archives
had posted questions focusing on specific content types such as “Could you
recommend a restaurant in [location name]?” Therefore, the method that used theresults of content-type classification was effective when QA archives were alignedwith the guidebooks.
Table 9. Ration of enriched guidebook pages that were judged to be “Appropriate”
MethodBaseline method
Our method 1
Our method 2

Blog Entry (%)
72
82
82

QA Archive (%)
53
57
77

6 Conclusions
In this paper, we proposed a method for enriching guidebooks by aligning them with blog
entries and QA archives. We conducted three experiments: (1) content-type classification ofguidebook pages, blog entries, and QA archives, (2) alignment of blog entries and QA archives
with guidebooks, and (3) alignment of blog entries and QA archives with a guidebook page. In
the experiment (1), we found that our method using both textual and image information(IG+BoVW) obtained the best performance. In the experiment (2), our method KNN_TYPE
obtained best precision scores in both aligning with blog entries and QA archives. In theexperiment (3), judges considered 82.0% of blog entries and 77.0% of QA archives to be
helpful in out method 2 (coarse-to-fine, with content type). With these steps, it was possible toalign relevant information with pages of guidebooks and help travellers collect tourist
information. We mentioned the method for enriching guidebooks by SNS, but we can easily
apply this method to various other documents, such as travel leaflets or booklets.

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