Automatic Identification of Know-How Blog Entries from a Travel Blog Database
Hidetsugu Nanba

Saki DoukeToshiyuki Takezawa

Graduate School of Information Sciences, Hiroshima City University
Hiroshima, Japan{nanba, douke, takezawa}@ls.info.hiroshima-cu.ac.jp
Abstract—Tourists in foreign countries must keep in mind
differences in culture and customs of the country they arevisiting to avoid any trouble that may arise from thesedifferences and to enjoy and appreciate the culture. We
propose a method for identifying blogs that offer travel knowhow from a travel blog database. To investigate theeffectiveness of our method, we conducted some experiments.
From the experimental results, we obtained precision of 0.619,
recall of 0.928, and F-measure of 0.743.
Keywords-component; blog; travel know-how; SVMI.

INTRODUCTION

Tourists visiting foreign countries must keep in mind
differences in culture and customs of the new country to help
them avoid any trouble that may arise from these differences.
In this paper, we propose a method to identify blogs
containing travel know-how from a travel blog database.
The travel know-how blog entries can be divided into thefollowing two types:
1.
Event-type entries: These entries mention uniquetravel-related events, such as a traditional Japanese tea
ceremony. These blog entries give information about
the backgrounds or history of the events, manners
including dress code for attending the events, how toattend, and time required for the events.
2.
Procedure-type entries: Examples are “how to climb
Mt. Fuji” and “how to put on a traditional Japanesekimono.”Acquiring travel know-how through automatically
identified know-how blog entries is useful not only foravoiding trouble caused by a lack of information but also forenjoying and appreciating the culture of a country to bevisited.
The remainder of this paper is organized as follows.
Section II describes related work. Section III explains ourmethods. To investigate the effectiveness of our methods, weconducted some experiments, and Section IV reports onthese and the results. Section V concludes.
II.

RELATED WORK

Kozawa et al. [1] proposed a method for acquiring knowhow information by focusing on each object and how it isused. For example, when object names, such as “hair dryer”
or “thermometer,” are given to their system, it outputs thecorresponding know-how information, such as “to heatsomething” and “to monitor a temperature”, respectively,
which are automatically extracted from the Web. In contrast
to their approach, we focus on travel, and identify travelknow-how blog entries that mention about unique events andprocedures as described in Section I.
Inui et al. [2] proposed a method for collecting instancesof personal experiences as well as opinions from blogs. An
example of such a personal experience is the following.
On my way home, I (in a wheelchair) could not find myway out of Totsuka Station because all the elevators inthe station building stop running at 11 pm.
Such personal experiences are considered useful for
tourists. However, we focus on not only personal
experiences but also knowledge including historical or wellknown facts related to some events.
III.

IDENTIFICATION OF KNOW-HOW BLOGS FROM A
TRAVEL BLOG DATABASE

We use several cue phrases for identifying know-how
blog entries. We now describe how to collect these cuephrases.
A. Manual Method
To collect cue phrases, we used a travel blog database“TravelBlog” 1 , which provides more than 600,000 travel
blog entries written in English. Among these entries, we
randomly selected 20,000 entries containing the phrase “howto.” We then investigated features of know-how blog entries,
and found that five types of cue phrases in them. These areillustrated with some examples of cue phrases as follows.
ll
l
l
l

Methodology: how to make, how to get, how to eat
Procedure: Step 1, step one, 1)
Schedule: annual, national holiday
Venue: festival inAction: take place, attend

B. Semiautomatic Method
To collect more cue phrases, we focused on theexpression “how to.” Some expressions, such as “how to eat”
and “how to attend,” are useful for identifying know-how
blog entries, while some expressions, such as “how to be”
and “how to explain” may not be useful for identification.
We therefore collected candidates of cue phrases by applying
N-gram statistics to the 20,000 blog entries above, andmanually selected cue phrases from the candidates. Finally,
we obtained 54 cue phrases in total, a few of which are
shown in Table I.

1

http://www.travelblog.org

TABLE I.
EXAMPLE OF CUE PHRASES FOR TRAVEL KNOW-HOW
BLOG IDENTIFICATION

how to get to the
how to cook
how to makehow to play
how to eat
how to buy
IV.

EXPERIMENTS

To investigate the effectiveness of our methods, we
conducted several experiments.
A. Datasets and Experimental Setting
To generate the test data for identifying know-how andevent blog entries, we used the 426 blog entries containing
the phrase “how to” in “TravelBlog.” Then, we manuallyidentified 222 know-how blog entries. For the identification,
we employed the following criterion.
Even if a blog entry contains know-how information, we
do not identify it as a know-how blog entry if more thanhalf of it is irrelevant description.
B. Machine Learning and Evaluation Measures
We performed a twofold cross-validation test. We used
TinySVM 2 as the machine-learning package and used apolynomial kernel of degree two. As evaluation measures,we used precision, recall, and F-measure.

Generally, our methods could obtain high recall values,
while precision values are low. We therefore discuss the low
precision values. Figure 1 is a typical entry, which ourmethod CUE mistakenly identified as a know-how blog
entry. In this entry, two cue phrases appear (underlined), andas a result, CUE identified it as a know-how blog. This entry
comprises two subtopics: (1) A ninja show, and (2) a coffee
shop that the blogger visited after the ninja show. The blog
thus disobeys the criteria in Section IV-A. This strict
criterion is the main reason for the low precision value. In
our future work, we will investigate the automatic extractionof travel know-how passages from each entry that is
identified by our method.
Title: Ninja Show
On my final day in Japan, I hung out in Iga and finally got towatch the Ninja show. It was amazing watching ninja's fighteach other, demonstrate how to use weapons, and even havekids come up to hold katanas.
(snip)The younger ninja demonstrated how to throw one, two, andthree shurikan at once.
(snip)Aside from the ninja show we discovered a new coffee shopwhere we enjoyed a pastry for “lunch”.
(snip)

C. Alternatives
We conducted tests using the following three methods
and a baseline method.
Our methodsl
CUE: Use cue phrases, such as those in Section III-Aas features for machine learning.
l
N-GRAM: Use cue phrases such as those in Table I inSection III-B as features for machine learning.
l
CUE+N-GRAM: Use the cue phrases in Sections III-Aand III-B as features for machine learning.
Baseline methodl
BASE: Identify all blog entries as know-how blogentries.
D. Results and Discussion
Table II	 shows the experimental results. The F-measurescore by our method CUE was 0.058 higher than that of thebaseline method, confirming the effectiveness of our method
for identifying know-how blog entries.
TABLE II.

CUE
N-GRAM
CUE+NGRAM
BASE

2IDENTIFICATION OF TRAVEL KNOW-HOW BLOG ENTRIESPrecision
0.619
0.558

Recall
0.928
0.946

F-measure
0.743
0.702

0.610

0.881

0.721

0.521

1.0000.685

http://chasen.org/~taku/software/TinySVM/Figure 1. Example of mistakenly identified blog entry.

V.

CONCLUSION

In this paper, we proposed a method for identifying
know-how blog entries in a travel blog database. From theexperimental results, our method CUE obtained precision of0.619, recall of 0.928 and F-measure of 0.743, confirming
the effectiveness of our method.
REFERENCES
[1]

[2]

S. Kozawa, K. Uchimoto, and S. Matsubara, “Acquisition of KnowHow Information from Web,” Lecture Notes in Computer Science,Vol. 7097, pp.446-457, 2011.
K. Inui, S. Abe, H. Morita, M. Eguchi, A. Sumida, C. Sao, K. Hara, K.
Murakami, and S. Matsuyoshi, “Experience Mining: Building aLarge-Scale Database of Personal Experiences and Opinions fromWeb Documents,” Proc. the 2008 IEEE/WIC/ACM International
Conference on Web Intelligence, pp.314-321, 2008.