Automatic Extraction of Basis Expressions thatIndicate Economic Trends
Hiroki Sakaji1 , Hiroyuki Sakai1 , and Shigeru Masuyama1
Toyohashi University of Technology, 1-1 Hibarigaoka, Tempaku-cho, Toyohashi-shi,
Aichi 441-8580, Japan(sakaji,sakai,masuyama)@smlab.tutkie.tut.ac.jp

Abstract. This paper proposes a method to automatically extract basis expressions that indicate economic trends from newspaper articles byusing a statistical method. We also propose a method to classify theminto positive expressions that indicate upbeat, and negative expressions
that indicate downturn in economy, respectively. It is important for companies, governments and investors to predict economic trends in orderto forecast revenue, sales of products, prices of commodities and stock
prices. We considered that basis expressions are useful for the companies,governments and investors to forecast economic trends. We extracted
basis expressions, and classiﬁed them into positive expressions or negative expressions as information to forecast economic trends. Our method
used a bootstrap method that was minimally a supervised algorithm forextracting basis expressions. Moreover, our method classiﬁed basis expressions into positive expressions or negative ones without dictionaries.

1Introduction

It is important for companies, governments and investors to predict the economic
trends in order to forecast revenue, sales of products, prices of commodities andstock prices. The diﬀusion index1 is one of indices concerning economic trends,
and is computed every three months, and provides economic trends during priorperiod. However, it is diﬃcult to forecast the business performance accurately
by using diﬀusion indices, as it can not indicate current economic trends.
These indices are computed using numeric data. However, some qualitative
language data that reﬂect economic trends may not be quantiﬁed straightforwardly. For example, an opinion “Economy seems to recover” in a newspaper
article is hard to be quantiﬁed, as “Economy seems to recover” is a sense of thewriter.
Nakajima et al.[1] proposed a method for extracting articles concerning economic trends from newspaper articles and classifying them into positive articles
1The diﬀusion index is a summary measures designed to facilitate the analysis andforecast of business cycles by combining the behavior of a group of economic indicators that represent widely diﬀering activities of the economy, such as productionand employment, and that correspond closely to turning points.
http://www.esri.cao.go.jp/en/stat/di/di2e.html

that indicate upbeat in economy and negative ones that indicate downturn ineconomy. However, Nakajima’s method can not classify articles having two different opinions. For example, an article that indicates economy in Aichi prefecture is upbeat while that in Gifu prefecture is downturn, includes two diﬀerent
opinions about diﬀerent areas, and can not be treated by Nakajima’s method.
We propose a method to extract basis expressions that indicate economic
trends from newspaper articles concerning economic trends and to classify basis
expressions into positive or negative expressions. We considered that opinions
concerning economic trends can be extracted by using basis expressions, whichenable us to distinguish two diﬀerent types of opinions in the same articles. Our
method used a bootstrap method that was minimally supervised algorithm forextracting basis expressions. Moreover, our method classiﬁed basis expressions
into positive expressions or negative ones without dictionaries.

2Related WorkAs related work for extracting phrases that have a particular meaning, Kanayama
et al. proposed a method for extracting a set of sentiment units by using transferbased machine translation engine replacing the translation patterns with sentiment patterns[5]. However, to construct a complete list of complex rules orpatterns manually, which is the case of the above methods, is a time-consuming
and costly task. In contrast, our method uses statistical information and only
one initial clue phrase as an initial input. The domain-speciﬁc dictionaries, predetermined patterns, complex rules made by hand are not needed.
Wilson et al. proposed a method for determining whether an expression isneutral or polar[6]. In their research, the expressions are extracted manually andthe method needs dictionaries. In contrast, our method automatically extracts
expressions and does not need dictionaries.
Sakai et al. proposed a method for extracting cause information from Japaneseﬁnancial articles concerning business performance[3]. Their work is probably
most closely related to ours. However, our method extracts basis expression concerning not performance of each company but economic trends. Moreover, ourmethod also classiﬁes basis expressions into positive and negative ones.

3

Extraction of Basis Expressions

As a preprocessing, our method extracts articles concerning economic trends
from newspaper corpus by using Support Vector Machine(SVM)[4]. We applieda method proposed by sakai et al.[2] for extracting them. As a result, 10,027
newspaper articles concerning economic trends were extracted from Nikkei newspapers published from 1990 to 2005.
Here, a basis expression is a part of a sentence consisting of some “bunsetu’s”(a bunsetu is a basic block in Japanese composed of several words). Our method
extracts basis expressions by using clue phrases, i.e. phrases frequently modiﬁedby basis expressions. For example, a basis expression frequently modiﬁes clue

phrase ”の影響 (no eikyou: inﬂuenced by)” in Japanese. Our method extracts anexpression that consists of a clue phrase and a phrase that modiﬁes it as a basis
expression. Hence, if many clue phrases eﬀective for extracting basis expressions
are acquirable, basis expressions are extracted automatically. However, it is hardto acquire suﬃcient clue phrases eﬀective for extracting basis expressions manually. Hence, our method also acquires such clue phrases automatically from a
set of articles concerning economic trends.
Our method for extracting basis expressions is as follows.
Step 1: Input an initial clue phrase ”の影響 (no eikyou: inﬂuenced by)” andacquire phrases that modify them.
Step 2: Extract phrases appearing frequently in a set of the phrases acquiredin Step 1 (e.g. 世界経済 (sekai keizai: world economy)). In this paper, sucha phrase extracted in Step 2 is deﬁned as a “frequent phrase”.
Step 3: Acquire new clue phrases modiﬁed by the frequent phrases.
Step 4: Extract new frequent phrases from a set of phrases that modify thenew clue phrases acquired in Step 3. This step is the same as Step 2.
Step 5: Repeat Steps 3 and 4 until they are executed predetermined times orneither new clue phrases nor new frequent phrases are extracted.
Step 6: Extract basis expressions by using extracted frequent phrases and acquired clue phrases.
3.1

Extraction of Frequent Phrases

The method for extracting ”frequent phrases” from a set of phrases that modify
clue phrases is described below.
Step 1: Acquire a bunsetu modifying a clue phrase and eliminate a case particle
from the bunsetu. Here, the bunsetu is denoted by c.
Step 2: Acquire frequent phrase candidates by adding bunsetu modifying c toc. (See Figure 1.)
Step 3: Calculate score Sf (e, c) of frequent phrase candidate e containing c by
the following Formula 1.
Step 4: Adopt e assigned the best score Sf (e, c) among the set of frequentphrase candidates containing c as a frequent phrase.
Score Sf (e, c) is calculated by the following Formula 1:
Sf (e, c) = −fe (e, c)fp (e) log2 P (e, c),(1)where P (e, c) is the probability that frequent phrase candidate e containing cappears in the set of articles concerning economic trends. fe (e, c) is the numberof frequent phrase candidate e’s containing c in the set of articles concerning
economic trends. fp (e) is the number of bunsetu’s that compose e. P (e, c) iscalculated by the following Formula 2.
P (e, c) =fe (e, c),N e(c)(2)where N e(c) is the total number of frequent phrase candidates containing c in
the set of articles concerning economic trends.

Fig. 1. Examples of frequent phrase candidates

3.2

Selection of Frequent Phrases

The frequent phrases extracted from a set of phrases that modify clue phrases
may contain inappropriate ones. Hence, our method selects appropriate frequentphrases from them. Here, our method calculates entropy H(e) based on P (e, s)
and selects frequent phrases assigned entropy H(e) larger than a threshold value
calculated by Formula 5. P (e, s) is the probability that frequent phrase e modiﬁesclue phrase s. Entropy H(e) is used for reﬂecting “variety of clue phrases modiﬁedby frequent phrase e”. If entropy H(e) is large, frequent phrase e modiﬁes various
kinds of clue phrases and such a frequent phrase is an appropriate frequentphrase. Entropy H(e) is calculated by the following Formula 3.
∑H(e) = −P (e, s) log2 P (e, s),(3)s∈S(e)

whereP (e, s) = ∑f (e, s).
f (e, s0 )(4)

s0 ∈S(e)Here, S(e) is the set of clue phrases modiﬁed by frequent phrase e. f (e, s) is thenumber of frequent phrase e’s that modiﬁes clue phrase s in the set of articles
concerning economic trends. The threshold value is calculated by the following
Formula 5.
Te = α log2 |Ns |,(5)where Ns is the set of clue phrases used for extracting frequent phrases and α
is a constant (0 < α < 1).

3.3

Acquisition of Clue Phrases

The method for acquiring new clue phrases from frequent phrases is as follows.
Step 1: Extract a bunsetu modiﬁed by frequent phrase e.
Step 2: Acquire clue phrase s by adding a case particle contained in the frequentphrase e to the bunsetu.
Step 3: Calculate entropy H(s) based on the probability P (s, e) that clue phrases is modiﬁed by frequent phrase e.
Step 4: Select clue phrase s assigned entropy H(s) larger than a threshold value
calculated by Formula 7.
Here, entropy H(s) is introduced for selecting appropriate clue phrases and is
calculated by the following Formula 6.
H(s) = −∑P (s, e) log2 P (s, e),f (s, e).
0e0 ∈E(s) f (s, e )P (s, e) = ∑e∈E(s)(6)

Here, E(s) is the set of frequent phrases that modify clue phrase s, and f (s, e) is
the number of clue phrase s’s modiﬁed by frequent phrase e in the set of articles
concerning economic trends. The threshold value is calculated by the following
Formula 7.
Ts = α log2 |Ne |.

(7)

Here, Ne is the set of frequent phrases used for extracting clue phrases. α is thesame constant that in Formula 5.
3.4

Extraction of Basis Expressions by Using Frequent Phrases andClue Phrases

Finally, our method extracts basis expressions by using frequent phrases andclue phrases. A basis expression consists of a phrase that modiﬁes the cluephrase. Moreover, the phrase that modiﬁes the clue phrase contains some frequent phrases. For example, “輸出の減少を背景に (yusyutu no gennsyou wo haikei
ni: under decreasing export)” is a basis expression since phrase “輸出の減少(yusyutu no gennsyou: decreasing export)” modiﬁes clue phrase “を背景に (wohaikei ni: under)” and the phrase contains frequent phrase “減少 (gensyou: decreasing)”.

4

Classiﬁcation of Basis Expressions

Our method classiﬁes extracted basis expressions into positive expressions andnegative expressions. However, extracted basis expressions contain some of inappropriate basis expressions. As a result, our method extracted basis expressions
into positive expressions, negative expressions and other expressions. Other expressions are extracted basis expressions that are neither positive nor negativeexpressions.

For example, “同時拡大を続ける世界経済 (doujikakudai wo tudukeru sekaikeizai:
world economy continuing simultaneous expansion)” is a positive expression.
Thus positive expressions indicate that Japanese economy is upbeat. For example, “設備投資や個人消費の鈍化 (setubitoushi ya kojinsyouhi no donka: slowdown
of business investment and personal consumption)” is a negative expression.
Thus negative expressions indicate that Japanese economy is downturn. For example, “調査対象変更 (tyousataisyouhenkou: change of objective for survey)” and“景気の伸び悩み (keiki no nobinayami: stagnation of economy)” are other expressions. We deﬁne expressions that cite Japanese economy are inappropriate
as basis expressions, because our goal is extraction of basis expressions.
We develop two classiﬁers by using one-versus-rest method and Support Vector Machine(SVM)[4]. The one classiﬁes extracted basis expressions into positive
expressions and the others. Here, a positive expression is deﬁned as a correct
expression and the other is deﬁned as an incorrect expression. The other one
classiﬁes extracted basis expressions into negative expressions and the others.
Here, negative expression is deﬁned as a correct expression and the other is deﬁned as an incorrect expression. The classiﬁers use character N-gram and word
N-gram as features.

5

Evaluation

In this section, we evaluate our method. Our method extracted basis expressions
from 10,027 newspaper articles concerning economic trends and classify theminto positive and negative.
First, we evaluated our method for extracting basis expressions. We employ
CaboCha2 as a Japanese parser. We manually extracted 75 basis expressions
from 100 articles concerning economic trends performance as a correct data set.
Moreover, we extracted basis expressions by our method from the same 100 articles and calculated precision and recall. Here, a basis expression extracted by ourmethod is correct if it contains a basis expression extracted as the correct dataset. The precision, recall and F-measure3 calculated by the following formulas.
P recision =|Sb ∩ Ab||Sb ∩ Ab|, Recall =,|Sb ∩ N b|
|Ab|where Sb is the set of basis expressions extracted by our method from 100 articles
concerning economic trends. Ab is the set of basis expressions contained in thecorrect data set. N b is the set of expressions modifying clue phrases in the 100
articles concerning economic trends. The results are shown in Tabel 1.
Next, we evaluated our method for classifying basis expressions. We employ
ChaSen4 as a Japanese morphological analyzer, and SV M light5 as an implementation of SVM. We extracted 1620 basis expressions by our method with α2

http://chasen.org/˜taku/software/cabocha/
F − measure = (2 × P recision × Recall)/(P recision + Recall)4
http://chasen.aist-nara.ac.jp/hiki/ChaSen/
5
http://svmlight.joachims.org

3

Table 1. Precision, Recall and F-measure of basis expressions extraction
α Precision Recall F-measure num. of basis expression
0.9
1.000 0.160
0.276
650
0.60.714 0.333
0.455
1620
0.50.042 0.573
0.07849293

Table 2. Precision, recall and F-measure of basis expression classiﬁcation with character N-gram feature

Positive
Negativenum. of frequent features Precision Recall F-measure9021
0.800 0.615
0.695
9021
0.843 0.855
0.849

0.6 and iteration count 3. 1620 basis expressions were manually annotated with“positive”, “negative” or “others”. The annotated basis expressions were divided
into two sets. The ﬁrst (1120 expressions) were a training data, used for feature
selection and modeling. We used the second set (500 expressions) as a test data
set. We calculated precision, recall and F-measure from the test data set. Theprecision and recall are calculated by the following formulas.
P recision =|E ∩ C|
|E ∩ C|, Recall =.
|E||C|

Here, E is the set of basis expressions annotated with correct expressions in thetest data set. C is the set of correct expressions contained in the test data set.
The results are shown in Tables 2 and 3.

6Discussions

In Table 1, precision rises from α 0.5 to 0.6, while recall drops. When low α
value is assigned, inappropriate clue phrases and frequent phrases were foundin a set of extracted clue phrases and extracted frequent phrases. Furthermore,
new inappropriate ones are extracted by extracted inappropriate phrases. As aresult, our method acquires many inappropriate ones. This happens when α is
between 0.5 and 0.6.
In Tables 2 and 3, focusing on recall, it is interesting to note that negativeclassiﬁers perform better than positive ones. This is due to the fact that 1620
basis expressions contain negative expressions far more than positive ones. We
consider that cause for few positive expressions is due to recession in Japanduring the period of the corpus.
In Tables 2 and 3, focusing on precision, character N-gram feature classiﬁer
is the highest of all. This is due to the fact that characters play a key role inclassifying expressions in Japanese, because Chinese characters that are one of
the Japanese characters have meanings.

Table 3. Precision, recall and F-measure of basis expression classiﬁcation with word
N-gram feature

Positive
Negative

7

num. of frequent features Precision Recall F-measure
6450
0.769 0.545
0.638
6450
0.833 0.867
0.845

Conclusion

We proposed a method for extracting basis expressions that indicate economictrends from Japanese newspaper articles concerning economic trends. First, ourmethod extracts basis expressions from them by using statistical information andinitial clue phrases. Next, our method classiﬁes basis expressions into positiveexpressions, negative expressions and other expressions. This method can also
be applied to other tasks such as extracting reputations for speciﬁc items.
Acknowledgment
This work was supported in part by Global COE Program “Frontiers of Intelligent Sensing ”, MEXT, Japan.

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