Automatic Compilation of an Online Travel Portal
from Automatically Extracted Travel Blog Entries
Aya Ishinoa,
Hidetsugu Nanbaa
Toshiyuki TakezawaaaGraduate School of Information Sciences, Hiroshima City University,
Hiroshima, Japan{ishino, nanba, takezawa}@ls.info.hiroshima-cu.ac.jp

Abstract
For travelers who plan to visit a particular tourist spot, information about it is required.
In this paper, we propose a method for extracting and organizing appropriate
information from weblogs (blogs). Recently, increased numbers of travelers havebeen writing of their travel experiences via blogs. We call these travel blog entries,
and they contain much useful travel information. For example, some bloggers
introduce useful web sites for a tourist spot, while others report on transportation
between tourist spots. Here, we extract hyperlinks of web sites for tourist spots fromtravel blog entries and organize them via automatic classification. We also extract
transportation information automatically from travel blog entries. To investigate theeffectiveness of our method, we conducted experiments. For the extraction oftransportation information, we obtained an 80.3% for Precision. For the classificationof hyperlinks, we obtained a high Precision. Finally, we constructed a prototype
system, which provides information about (1) transportation between tourist spots and(2) useful web sites for tourist spots.
Keywords: Blog; Information Extraction; Travel Information; Link Classification

1 Introduction
For travelers who plan to visit a particular tourist spot, information about the place isnecessary. Travel guidebooks and portal sites provided by tour companies andgovernmental tourist boards are useful information sources about travel. However, it
is costly and time-consuming to compile travel information for all tourist spots and tokeep this data up to date manually. Therefore, we have studied the automaticcompilation of an online travel portal, which provides useful web sites for travel, andtransportation information.
For this compilation, we focused on travel blog entries, which are defined as travel
journals written by bloggers in diary form. Travel blog entries are considered a useful
information source for obtaining travel information, because many bloggers’ travel
experiences are written in this form. For example, some bloggers introduce useful
web sites for a tourist spot, while others report on transportation between tourist spots.

Nanba et al. (2009) identified travel blog entries in a blog database, then extracted
pairs comprising a location name and a local product from these entries. In this paper,
we propose a method that extracts transportation information from travel blog entries,
which are identified automatically by Nanba’s method. From these entries, we also
extract the hyperlinks by which bloggers describe useful web sites for a tourist spot,
and thereby construct collections of hyperlinks for a tourist spot.
The remainder of this paper is organized as follows. Section 2 shows the systembehavior in terms of snapshots. Section 3 discusses related work. Section 4 describes
our methods. To investigate the effectiveness of our methods, we conducted someexperiments, and Section 5 reports the experimental results. We present someconclusions in Section 6.

2 System Behavior
In this section, we describe our prototype system, which provides information about(1) transportation between tourist spots and (2) useful web sites for tourist spots.These
are the steps in the search procedure.
(Step 1) Input the location name for a tourist spot, such as “Hiroshima”, in the searchform (shown as ① in Figure 1).
(Step 2) Click the “search” button (shown as ②) to generate a list of transportation
options, such as “Hiroshima → Osaka” and “Hiroshima → Tokyo”, for the location
name.
(Step 3) Click the “link” button (shown as ③) to generate a list of URLs for web sites
related to the location together with automatically identified link types and the context
of citations (we call them “citing areas”), by which the authors of travel blog entries
describe the sites. Figure 2 shows a list of links related to “Osaka”.

3 Related WorkIn this section, we describe some related studies on geographic information retrieval,extraction of transportation information, and link classification.


Geographic Information Retrieval

GeoCLEF (http://ir.shef.ac.uk/geoclef/) is the cross-language geographic retrieval
track run as part of the Cross Language Evaluation Forum (CLEF), and it has been
operating since 2005 (Gey et al., 2005). The goal of this task is to retrieve news
articles relevant to particular aspects of geographic information, such as “wineregions around the rivers in Europe”. In our work, we focus on travel blog entries
rather than news articles, because bloggers’ travel experiences tend to be written as
travel blog entries.

①②
Hiroshima

Search Form for destination

Hiroshima →Osaka③

Hiroshima →Tokyo

Fig. 1. The travel information search system

Link type(Restaurant)
URL

Citing areas(Spot) (Restaurant)

Fig. 2. A list of web sites for a travel spot.

Extraction of Transportation Information

Davidov (Davidov, 2009) presented an algorithm framework that enables automatedacquisition of map-link information from the Web, based on surface patterns such as“from X to Y”. Given a set of locations as initial seeds, they retrieved from the Web
an extended set of locations, and produced a map-link network that connects theselocations using transport-type edges. In this paper, we propose a method forextraction of transportation information via machine-learning techniques.
 Link Classification
There have been several reports on research that automatically classifies links in blog
entries (Kale et al., 2007; Martineau & Hurst, 2008). Kale devised a method thatclassifies links in blog entries as positive or negative, using manually created rules(Kale et al., 2007). Alternatively, Martineau proposed a machine-learning approach
for link classification from several viewpoints using words that appear in the context
of citations of URLs as features. In our work, we classify links into four categories oftravel, which we will describe in Section 4.2.3, using a machine-learning technique.

4 Automatic Compilation of an Online Travel Portal
The task of compiling travel information is divided into three steps: (1) identificationof travel blogs, (2) extraction of transportation information, and (3) classification oflinks in travel blog entries. For Step 1, we use Nanba’s method (Nanba et al., 2009).
Steps 2 and 3 are explained in Sections 4.1 and 4.2.
4.1 Extraction of Transportation Information
We use information extraction based on machine learning to extract information, suchas “a departure place”, “a destination”, or “a transportation device”, from travel blog
entries. First, we define the tags used in our examination.
•
•
•
•
•

FROM tag includes a departure place.
TO tag includes a destination.
VIA tag includes a route.
METHOD tag includes a transportation device.
TIME tag includes the time for transportation.

This is a tagged example.
[original]<FROM>広島</FROM>から<TO>大阪</TO>まで<TIME>5 時間</TIME>かけて、<METHOD>バス</METHOD>で行った。
[translation]
It took <TIME>five hours</TIME> from <FROM>Hiroshima</FROM>
to<TO>Osaka</TO> by <METHOD>bus</METHOD>.

We formulate the identification of the class of each word in a given sentence andsolve it using machine learning. For the machine-learning method, we opted theConditional Random Fields (CRF) method (Lafferty, McCallum, & Pereira, 2001),
whose empirical success has been reported recently in the field of natural language
processing. The CRF-based method identifies the class of each entry. Features andtags are used in the CRF method as follows: (1) k tags occur before a target entry, (2)k features occur before a target entry, and (3) k features follow a target entry. We
used the value k = 4, which was determined via a pilot study. We use the following 15
features for machine learning. A sequence of nouns (a noun phrase) was treated as anoun. We used MeCab (http://mecab.sourceforge.net/) as a Japanese morphologicalanalysis tool to identify the part of speech.
••••

A word.
Its part of speech.
Whether the word is a quotation mark.
Whether the word is a cue phrase, detail as follows.
Tag
FROM

FROM
TO

TO
VIA

METHODTIME

Cue phase
Whether the word is a cue that often appears immediately after
the “FROM” tag, such as “から” (from) or “を出発” (left).
Whether the word is frequently used in the name of a tourist
spot, such as “博物館” (museum) or “遊園地” (amusementpark).
Whether the word is frequently used in the name of adestination, such as “観光” (sightseeing tour) or “駅” (station).
Whether the word is the name of a tourist spot.
Whether the word is the name of a station or airport.
Whether the word is a cue that often appears immediately after
the “TO” tag, such as “まで” (to) or “に到着” (arrival).
Whether the word is a cue that often appears immediately after
the “via” tag, such as “経由” (via) or “通って” (through).
Whether the word is the name of a highway.
Whether the word is the name of a transportation device, suchas “飛行機” (airplane) or “自動車” (car).
Whether the word is the name of a vehicle.
Whether the word is the name of a train or bus.
Whether the word is an expression related to time, such as “分”(minute) or “時間” (hour).

The numberof cues
40
45
11
13,779
9437
271
43
101
148
128
2033
77

4.2 Link Classification
The procedure for classifying links in travel blog entries is as follows.
1. Input a travel blog entry.
2. Extract a hyperlink and any surrounding sentences that mention the link (a citingarea).
3. Classify the link by taking account of the information in the citing area.

In the following, we will explain Steps 2 and 3.
4.2.1
Extraction of citing areas
We manually created rules for the automatic extraction of citing areas. These rules
use cue phrases. When authors of travel blog entries introduce web sites, quotation
marks or brackets are often used immediately before and after the title of the site. The
authors also use particular words, such as “紹介” (introduction), “公式サイト”(official site), or “の HP” (web page of), or particular marks, such as quotation marks
or brackets. Therefore, we manually selected 26 cues and used them for citing area
extraction using the following rules.
1. Extract a sentence that includes the link.
2. Extract X sentences that appear before or after a web hyperlink, and add them tothe candidate. Here, we used the value of X = 2, which was determined via a pilot
study.
3. Extract keywords from the candidate area in Step2 using the following rules (a)and (b), if the area includes cues.
(a) Extract character strings within quotation marks or brackets as keywords.
(b) Extract character strings just before or after particular cues, such as “の HP”(web page of).
4. Extract all sentences including the keywords in the blog entry and the sentences
extracted in Step 2 as a citing area.
We explain these rules using the following travel blog entry.
[original]
1 チェックアウト後、いつものようにパパ＆ママの寄り道が始まります!!
2 ということで、まずは河津の【バガテル公園】に行ってきました☆
3 四季の蔵から、車で数分圏内にあります。
4 ワンコもお散歩 OK なので、犬連れには嬉しい場所です
5 メッチャ、綺麗でしたよ～♪
6 ※バガテル公園の HP は、こちら→
7 http://www.bagatelle.co.jp/index.html
8 ↑いうまでもなく、美しいバラの数々(写真)9 四季の蔵の朝ごはんがボリューム満点だから、これくらいで充分です!!
10 初めて来たバガテル公園ですが、ワンコ OK だし、
11 季節によってはお花が綺麗なのでいいかも～♪
12 ランチメニューも充実しているし、また今度も来ようっと(ノ∇≦*)キャハッッッ♪
[translation]
1 Dad and Mom started to take a side trip after the checkout!!
2 Firstly, we visited “Bagatelle Park” in Kawazu☆
3 It took a couple of minutes from Shikinokura by car.
4 In this park, we could take a stroll with dogs.
5 Very very beautiful♪
6 (*) Following is the web page of the Bagatelle Park
7 http://www.bagatelle.co.jp/index.html
8 ↑ Beautiful roses (pictures).

9 As we had a big breakfast in Shikinokura, the lunch in this park is enough!!
10 We visited the Bagatelle Park for the first time, and we could take a stroll with our dog
here.
11 And flowers are beautiful in high season♪
12 The lunch menu is abundant. I hope to come again :-) ♪

In Step 1, we extract sentence 7, which includes a hyperlink as an initial candidatearea. In Step 2, we also extract the two sentences that appear before and after thehyperlink (5, 6, 8, and 9) and add them to the candidate. In Step 3, we extract “バガテル公園” (Bagatelle Park), which appears just before a cue phrase “の HP” (webpage of), as a keywords. In Step 4, we add sentences 2 and 10, both of which include
the keyword “バガテル公園” (Bagatelle Park), to the candidate. Finally, we extract
the sentences 2, 5, 6, 7, 8, 9, and 10 as a citing area.
4.2.2
Definition of link types
We classify link types into the following four categories.
••••

S (Spot): Whether the information is about tourist spots.
H (Hotel): Whether the information is about accommodation.
R (Restaurant): Whether the information is about restaurants.
O (Other): Other than types S, H, and R.

It is possible to classify a hyperlink into more than one link type. For example, ahyperlink to “ ラ ー メ ン 博 物 館 ” (Chinese noodle museum,
http://www.raumen.co.jp/home/) is classified into types S and R, because the visitors
to this museum can learn the history of Chinese noodles in addition to eating Chinese
noodles.
4.2.3

Method of link type classification

Here, we explain how to classify hyperlinks automatically. We employed a machinelearning technique using the following features. A sequence of nouns (a noun phrase)
was treated as a noun.
• A word.
• Whether the word is a cue phrase, detailed as follows, where the numbers inbrackets shown for each feature represent the number of cues.
Cues for type S
Cue phrase
A list of tourist spots, collected from Wikipedia.
Words frequently used in the name of tourist spots, such as “動物園” (zoo) or“博物館” (museum).
Words related to sightseeing, such as “見学” (sightseeing) or “散策” (stroll).
Other words.

The numberof cues
17,371
138
172
131

Cues for type H
Cue phrase
Words that are frequently used in the name of hotels, such as “ホテル” (hotel)
or “旅館” (Japanese inn).
Component words for accommodations, such as “フロント” (front desk) or“客室” (guest room).
Words that are frequently used when tourists stay in accommodation, such as“泊る” (stay) or “チェックイン” (check in).
Other words.

The numberof cues
929
1421

Cues for type H
Cue phrase
Dish names such as “omelet”, collected from Wikipedia.
Cooking styles such as “Italian cuisine”, collected from Wikipedia.
Words that are frequently used in the name of restaurants, such as “レストラン” (restaurant) or “食堂” (dining room).
Words that are used when taking meals, such as “食べる” (eat) or “おいしい”(delicious).
General words that indicate food, such as “ご飯” (rice) or “料理” (cooking).
Other words.

The numberof cues
2,779
114
21
52
31
31

5 Experiments
In order to investigate the effectiveness of our methods, we conducted two
experiments: (1) extraction of transportation information from travel blog entries, and(2) extraction and classification of hyperlinks. We report on these in Sections 5.1 and
5.2, respectively.
5.1 Extraction of Transportation Information
Data Sets and Experimental Settings
We randomly selected 10,000 sentences from 193 travel blog entries, and manuallyassigned tags to them, as described in Section 4.1. The number of manually assigned
tags
is
shown
in
Table
1.
We
used
CRF++
(http://www.chasen.org/~taku/software/CRF++) software as the machine-learning
package. We used Recall and Precision as evaluation measures.

Table 1. Numbers of manually assigned tags in the extraction of transportation
information
Training
136
384
58
245
87

FROM
TO
VIA
METHOD
TIME

Test
30
126
15
55
27

Results and Discussion
The evaluation results are shown in Table 2. As shown in the table, we obtained ahigh Precision. Among these results, both the Recall and Precision of “VIA” were low,which is due to the low frequency of this tag in both training and test data (Training:
58, Test: 15).
Table 2. Evaluation results for the extraction of transportation information
FROM
TO
VIA
METHOD
TIME
Total

Recall (%)
30.0
45.2
33.3
66.0
50.0
46.8

Precision (%)
75.0
75.0
55.6
94.9
87.6
80.3

There were two typical errors causing low Precision: (1) ambiguity of cues (69.6%)
and (2) the traveler’s desire (17.4%). We describe these errors as follows:(1) Ambiguity of cues (69.6%)
In the following example, the “VIA” tag was assigned to “結局お昼” (noon, after all),because a cue for VIA “過ぎて” (past) appears immediately before it. However, the“VIA” tag should not have been assigned in this case.

VIA[original](Correct) 結局お昼を過ぎても動かなかった。
(Analysis result) <VIA>結局お昼</VIA>を過ぎても動かなかった。
[translation](Correct) I did not move though it was past noon, after all.
(Analysis result) I did not move though it was past <VIA>noon, after
all</VIA>.

(2) The traveler’s desire (17.4%)
In the following example, the “METHOD” tag was mistakenly assigned to“Shinkansen bullet train”, which the visitor did not actually use. This was because the“METHOD” cue “で帰る” (return by) appears immediately before it.

METHOD[original](Correct) 新幹線で帰るのがベターだったのですが、ちょっとは旅気分を味わいたいということで、別ルートで。
(Analysis result) <METHOD>新幹線</METHOD>で帰るのがベターだったのですが、ちょっとは旅気分を味わいたいということで、別ルート
で。
[translation](Correct) It was better to return by Shinkansen bullet train, but I chose anotherroute, because I wanted to draw out the journey.
(Analysis result) It was better to return by <METHOD>Shinkansen bullet
train</METHOD>, but I chose another route, because I wanted to draw out thejourney.

We now discuss the low Recall of our method. There were two typical errors for low
Recall: (1) the lack of contexts (59.1%) and (2) the lack of cues (17.3%). We describe
these errors as follows.
(1) The lack of contexts (59.1%)
In the following example, the “TO” tag should be assigned to “寺田屋” (Teradaya),but our method did not assign any tags to this word because there were no cues in thisshort sentence. To solve this problem, we need to take account of a longer context.
For example, the “TO” cue “来ました” (came), which appears in the previous
sentence, would be necessary for solving the problem in this case. However, taking
account of a longer context might result in lower Precision.

TO[original](Correct) いよいよ来ました！
「<TO>寺田屋</TO>」(Analysis result)いよいよ来ました！
「寺田屋」[translation](Correct) Finally I arrived!
“<TO>Teradaya</TO>”(Analysis result) Finally I arrived!!
“Teradaya”(2) The lack of cues (17.3%)
In the following example, the “FROM” tag was not assigned to “東京駅” (Tokyo
station), because “に別れを告げる” (I say good-bye to) was not included in the“FROM” cues. To increase the number of cues, a statistical approach is required.

FROM[original](Correct) 駅弁買い込んで<FROM>東京駅</FROM>に別れを告げる。
(Analysis result) 駅弁買い込んで東京駅に別れを告げる。
[translation](Correct) I bought a station lunch and said good-bye to <FROM>Tokyo
Station</FROM>.
(Analysis result) I bought a station lunch and said good-bye to Tokyo Station.

5.2 Extraction and Classification of Links
Data Sets and Experimental Settings
Among the 7,412 hyperlinks in 17,266 travel blog entries, we removed 2,987, whichlink to Wikipedia and news sites. These sites are easily classified into type O by their
URLs. We randomly selected 1,000 of the remaining 4,155 links, manually classified
them, and used them for our examination. Table 3 shows the number of hyperlinks foreach type. We performed a four-fold cross-validation test. We used TinySVM(http://chasen.org/~taku/software/TinySVM/) software as the machine-learning
package and used Recall and Precision as evaluation measures.
Table 3. The number of hyperlinks for each type
Link types
the number of
links

S

H

R

O

353

98

343

250

Alternatives
To investigate the effectiveness of our method, we classified link types using thefollowing two methods for citing area classification.
• Our method: Extract sentences by manually created rules, as described in Section
4.2.1.
• Baseline method: Extract the X sentences before and after the link.
Results and Discussion
We used a value of X = 2, which was determined via a pilot study, for the baseline
method. The evaluation results are shown in Table 4, where our method generallyshows improved Recall and Precision in comparison with the baseline method. In
particular, the Recall and Precision for link type S were significantly improved.
Table 4. Evaluation results for link classification
Link
Type
S
H
R
O

Baseline Method
RecallPrecision
(%)
(%)
54.5
64.7
63.3
79.8
72.3
76.0
59.2
42.2

Our Method
RecallPrecision
(%)
(%)
62.5
72.7
64.9
81.3
71.9
76.7
71.6
48.6

There were two typical errors in link classification: (1) the lack of cues and (2)ambiguity in cue phrases. We describe these errors as follows.
(1) Lack of cues
For the machine learning, we used manually selected cues, as described in Section
4.2.3. To improve the coverage of cues, a statistical approach, such as applying ngram statistics to a larger blog corpus, will be required.

(2) Ambiguity in cue phrases
We used “visit” (訪れた) as an S cue. However, “visit” (訪れた) is also frequently
used when a visitor eats in a restaurant, as in “食事を取るためレストランを訪れた ” (I visited the restaurant for taking a meal). Therefore, our method has
misclassified a link type R as a link type S.

6 Conclusion
In this paper, we have proposed two methods: (1) extraction of transportation
information from travel blog entries, and (2) extraction and classification of
hyperlinks in the travel blog entries. From our experimental results, we haveconfirmed the effectiveness of our methods. Finally, we have constructed a systemthat can search for a destination to which the user can travel from the present locationand that can provide links about the destination.
In this paper, we focused on travel blog entries written in Japanese. In our future work,
we will translate cue phrases from Japanese into other languages, and apply our
method into blog entries written in various languages.

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