Extraction of Trend Information from ewspaper Articles: Hiroshima City
University at TCIR-7 MuST
Hidetsugu anba
Hiroshima City University
3-4-1 Ozukahigashi, Hiroshima 731-3194 JAPAN

Phone: +81-82-830-1584
nanba@hiroshima-cu.ac.jp

Abstract
Trend information is a summarization of temporal
statistical data, such as changes in product prices andsales. We propose a method for extracting trend information from multiple newspaper articles. Ourgroup participated in the T2 Subtask at TCIR-7
MuST (Multimodal Summarization for Trend Information). Our goal was to evaluate the effectiveness of
our rule-based system using the data provided by
TCIR-7 MuST. From the experimental results, we
obtained precision of 62.9% and recall of 7.0%.
Keywords: Trend Information, Visualization, ewspaper article.

1

Introduction

Trend information is a kind of summarization of
temporal statistical data, such as changes in productprices and sales. We have constructed a system thatextracts trend information from multiple newspaper
articles and blogs [Nanba et al. 2007]. We modified
this system to improve its performance in trend information extraction, and submitted four results to the
T2N Subtask at NTCIR-7 MuST (Multimodal Summarization for Trend Information) [Kato and Matsushita 2008]. In this paper, we report and discuss theevaluation results in this subtask.
The remainder of this paper is organized as follows.
Section 2 explains how we extracted trend information from newspaper articles. Section 3 reports on theexperiments, and discusses the results. We present
some conclusions in Section 4.

2

Extraction of Trend Information

To extract trend information from newspaper articles, we modified our previous system [Nanba et al.
2007]. We explain the system in Section 2.1, and wepoint out its problems and their improvement in Section 2.2.

2.1

Extraction of Trend Information from
ewspaper Articles and Blogs

Nanba et al. [2007] constructed a system that extracts trend information from newspaper articles andblogs. The system consists of two modules: (1) amodule for temporal expressions extraction and (2) amodule for statistical values extraction. In the following, we describe both modules.

2.1.1 Module for Temporal Expressions Extraction
The module used CaboCha1 [Kudo and Matsumoto
2003] for extracting temporal expressions from texts.
CaboCha is a statistical syntactic parser for Japanese
texts, and also identifies eight kinds of named entity,
such as date, organization, and location, in texts. Here,not all temporal expressions in texts are used to generate a graph. For example, there are three temporal
expressions in the sentence in Figure 1, but "1994" isthe only expression that is used in generating a graphof "the number of births for one year".
一九九四年の年間出生数は前年より四万七千人
も多い百二十三万五千人を記録し，二十一年ぶ
りに大幅増に転じた．
(The number of births recorded in 1994 was
1,235,000, which was 47,000 greater than in the previous year, and this significant increase was the firstin 21 years.)
Figure 1. Example of a sentence that contains morethan one temporal expression
To eliminate unrelated temporal expressions fromtexts, we focused on some cue phrases, some of which
are shown in Figure 2. In the figure, cue phrases areunderlined. If these cue phrases appear before or after
temporal expressions, they are eliminated.
前年より (in comparison with the previous year)
10 年ぶり (for the first time in 10 years)
昨年以来 (since last year)
3 年連続で (for the third year in a row)
Figure 2. Examples of cue phrases to eliminate
unrelated temporal expressions
In the next stage, the extracted temporal expressions in the prior stage are converted into a specificformat "<DATE>YYYY-MM-DD</DATE>", where
YYYY, MM, and DD indicate year, month, and day,respectively. For example, if "昨日" (yesterday) is
extracted from a text written on January 25, 2007,
then the expression "yesterday" is replaced by"<DATE>2007-01-24</DATE>". If the exact date is
not provided, such as "昨年 12 月" (last December),this expression is converted as "<DATE>2006-12??</DATE>".
1

http://chasen.org/~taku/software/cabocha/

2.1.2 Module for Statistical Values Extraction
The module extracts statistical values in the following four steps.
1.
2.
3.
4.

Split a sentence into separate statistical values.
Annotate "NUM" and "UNIT" tags.
Eliminate unrelated statistical values.
Extract statistical values.

(Step 1) Split a sentence into separate statisticalvaluesSome sentences contain more than one statisticalvalue; for example:
今日のレギュラーガソリンは１３０円，ハイ
オクは１５０円だった．
(Today’s price of regular gasoline was 130 yen, andthat of premium gasoline was 150 yen.)
From this sentence, "150 yen" might be extracted mistakenly as the price of regular gasoline, or "130 yen"as that of premium gasoline. We therefore split sentences for their statistical values. First, we analyse thedependency structure of the sentence using CaboCha.
Second, we integrate two bunsetsus (segments) thathave a modification relation and are adjacent to eachother. We explain this process using the example
shown in Figure 3. This figure is the result from
CaboCha for the sentence given above. "Chunk" tagswith ID numbers are assigned to each bunsetsu. Here,the attribute values in each chunk tag indicate the ID
numbers of bunsetsus that have a modification relation. Bunsetsus 0 and 1 have a modification relation,
and are adjacent to each other. Therefore, these bunsetsus are integrated. By repeating the process to theend of the sentence, it is split into two parts: (1) "Today’s price of regular gasoline was 130 yen", and (2)"and that of premium gasoline was 150 yen". In thisstep, the system also adds an "NP" tag to all nounphrases in the text, based on the result from CaboCha.
<chunk id="0" link="1"><NP>今日</NP>の(Today’s)</chunk><chunk id="1" link="2"><NP>レギュラーガソリン</NP>は(price of regular gasoline was)</chunk><chunk id="2" link="4">130 円，(130 yen, )</chunk><chunk id="3" link="4"><NP>ハイオク</NP>は(andthat of premium gasoline was)</chunk><chunk id="4" link="–1">150 円だった．(150yen.)</chunk><chunk><NP>今日</NP>の<NP>レギュラーガソリ
ン</NP>は 130 円，(Today’s regular gasoline was
130 yen, )</chunk><chunk><NP>ハイオク</NP>は 150 円だった．
(and premium gasoline was 150 yen.)</chunk>
Figure 3. Splitting a sentence for statistical values(Step 2) Annotate "UM" and "UIT" tags
The system adds "NUM" and "UNIT" tags to all
candidate statistical values using the pattern "number+ (noun phrase | postfix | counter suffix)". For example, NUM and UNIT tags are annotated for an expression "150 円" (yen) as "<NUM>150</NUM><UNIT>円</UNIT>".
(Step 3) Eliminate unrelated statistical values
Not all expressions assigned "NUM" and "UNIT"
tags are used to generate a graph of trend information.
For example, there are two statistical values in thesentence in Figure 4, but "９２円" (92 yen) is the
only expression that is used to generate a graph.
石油情報センターが２３日発表した給油所石油製品市況調査によると，６月のガソリン価格は全国平均でレギュラー１リットル当たり<NUM>９２</NUM><UNIT>円</UNIT>となり，前月比で<NUM>２</NUM><UNIT>円</UNIT>上昇した．
(According to the Oil Information Centre’s survey ofmarket conditions for products sold through service
stations on (June) 23, the price of gasoline reached anational
averageof<NUM>92</NUM><UNIT>yen</UNIT>
per
litre,
regular;<NUM>2</NUM><UNIT>yen</UNIT> higher than
the average price of last month)(June 24, 1999, Mainichi newspaper article)
Figure 4. Example of an analysis of a newspaper
articleIn the same way as extraction of temporal expressions, unrelated statistical values are eliminated fromtexts using cue phrases, some of which are shown in
Figure 5. Here, cue phrases are shown as underlines.
If they appear before or after statistical values, they
are eliminated.
2 円高い (2 yen higher)
昨年比 45.2%上昇 (up 45.2% from the previous
year)
0.28 パーセント下落 (down 0.28 percent)Figure 5. Example of cue phrases to eliminate unrelated statistical values(Step 4) Extract statistical values
Pairs of statistical values and their nearest anterior
temporal expressions are extracted2 when one of the
keywords (the name of the statistic) and its associatedunit appear in the same integrated bunsetsu, and the
keyword is a prefix of one of the noun phrases in thebunsetsu. For example, "130 yen" was extracted fromthe sentence in Figure 3, when "レギュラー" (regular) and "円" (yen) were given to the system in advance, because "レギュラー (regular)" is a prefix of2

Nanba et al. [2007] reported that adequate temporal
expressions corresponding to each statistical value
were correctly extracted in all cases.

a noun phrase "レギュラーガソリン (regular gasoline)". We call this method the "prefix-searchmethod".

2.2

Problems of our Previous System andtheir Improvement

2.2.1 Problem with the Temporal Expressions Extraction Module
Most of the errors in our previous system werecaused by misconversion of temporal expressions,such as "前月" (the previous month), into "YYYYMM-DD" form, although temporal expressions weredetected correctly in most cases. We therefore employed another method, in addition to the method in
Section 2.1.1. The method considers the date that thearticle was written as the temporal information corresponding to all the statistical values in the article. We
call this method the "publication-date method".
2.2.2 Problem with the Statistical Values Extraction Module
Another typical error in our previous system wascaused by the fourth step in statistical values extraction. Although the prefix-search method obtained ahigh precision score, many statistical values were notextracted. We therefore propose another approach,called the "DP-matching method". This method extracts statistical values when a keyword and a unit
appear in the same integrated bunsetsu, and similarity
between the keyword and one of noun phrases exceeds a threshold value. To calculate the similarity,
we employed dynamic programming (also known as"edit distance") [Needleman and Wunsch 1970], andused 0.9 as the threshold value, which was determinedby preliminary experimental results using Nanba's
data [Nanba et al. 2007].

3

Evaluation

3.1

Submission

We submitted four systems: "HCU1", "HCU2","HCU3", and "HCU4" to the formal run of the T2N
Subtask at NTCIR-7 MuST [Kato and Matsushita
2008]. The differences between these systems are
summarized in Table 1.
Table 1. Summary of submitted systems
Statisticalvalues Temporal expresextraction
sions extraction
prefixDPrulepublicasearch
matchbased
tion
ing
date
HCU1
○
○
HCU2
○
○
HCU3
○
○
HCU4
○
○

3.2

Data and Evaluation

We used the data provided by the MuST organizers
for evaluation. They consist of eight topics including
120 newspaper articles in total. All the submitted systems were evaluated in terms of precision, recall, andF-measure [Kato and Matsushita 2008].

3.3

Results

We show the evaluation results for each system inTable 2. The results show that HCU1 and HCU2 obtained higher precision scores than HCU3 and HCU4.
Both HCU1 and HCU2 employed the prefix-searchmethod to extract of statistical values.

3.4

Discussion"Rule-based method" vs. "publication-date
method"To compare the methods for the extraction of temporal expressions, we count the number of errors foreach system. The results are shown in Table 3. The
number of errors for HCU2 and HCU4 are smaller
than those for HCU1 and HCU3, which indicates thatthe publication-date method is superior to the rulebased method.
Table 3. The number of errors for the extraction of
temporal expressions
HCU1
HCU2
HCU3
HCU4
12
3
36
23
"Prefix-search method" vs. "DP-matching
method"Generally, the numbers of statistics extracted by
HCU3 and HCU4 are much larger than those extracted by HCU1 and HCU2. We compared recalls of
HCU1 and HCU3, both of which employed the same
method (rule based) for the extraction of temporal
expressions. The recall of HCU3 is 7.7% better thanthat of HCU1. We also compared recalls of HCU2
and HCU4. The recall of HCU4 is 8.9% greater thanthat of HCU2. However, the precisions of both HCU3
and HCU4 are smaller than those of HCU1 and HCU2.
From these comparisons, we can conclude that the
DP-matching method contributes to improve recall,while it impairs precision.
Evaluation results for each topic
Precisions of HCU1 and HCU2 for the topics"Communication Device (T2N0103)", "I-mode(T2N0106)", and "Digital Camera (T2N0107)" aremuch better than those of HCU3 and HCU4. The
common feature of these topics is that two or morekinds of statistical values are mentioned in each article. For example, the number of "Personal Handyphone System (PHS)" subscribers is compared withthose of "cellular phone" and "land phone" in the"Communication Device" topic. In such cases, namesof statistics are often shortened. Figure 6 is a typicalexample in which the DP-matching method could not
extract statistical values correctly. In this article, threestatistics, "the number of cellular phone and PHS subscribers", "the number of cellular phone subscribers",
and "the number of PHS subscribers", were mentioned. However, the latter two are expressed as "cellular phone" and "PHS". In such cases, the DPmatching method could not contribute to improverecall, but rather impaired precision.

in the T2N Subtask at NTCIR-7 MuST, and submitted
several results provided by four different systems.
From the experimental results, we obtained precisionof 62.9% and recall of 7.0% with the prefix searchmethod for statistical values extraction and the publication date method for temporal expressions extraction.

総務省が１０日発表した４月の携帯電話とＰＨ
Ｓの加入者数は、前月比１・６％、１０９万３
０００人増の６７８７万７０００人となった。
(略)
携 帯電話 が１・ ８％増 の６ ２０３ 万７０ ００台、ＰＨＳが０・０３％減の５８４万台。

5

According to the report by the Ministry of Internal
Affairs and Communications on May 10, the numberof cellular phone and PHS subscribers became
67,877,000, which is 1.6% (1,093,000) larger than in
the previous month.
(snip)
Cellular phone is 62,037,000 (1.8% up), while PHS is5,840,000 (0.03% down).
Figure 6. Example that the DP-matching method
could not extract correctly
To improve the low precision by the DP-matching
method, the following procedure may be useful.
1.

When more than one statistic is mentioned in a
newspaper article,
Apply the prefix-search method.

2.

In other cases,
Apply the DP-matching method.

4

Conclusions

We have proposed a method for extracting trend information from multiple newspaper articles. To confirm the effectiveness of our system, we participatedAcknowledgements

The authors would like to express their gratitude to
the organizers of the MuST.

References
T. Kato and M. Matsushita. Overview of MuST at the
NTCIR-7 Workshop - Challenges to Multi-modal
Summarization for Trend Information - . In Proceedings of the 7th NTCIR Workshop Meeting on
Evaluation of Information Access Technologies:
Information Retrieval, Question Answering andCross-lingual Information Access, 2008.
T. Kudo and Y. Matsumoto. Fast Methods for Kernelbased Text Analysis. In Proceedings of the 41st
Annual Meeting on Association for Computational
Linguistics, pp.24-31, 2003.
H. Nanba, N. Okuda, and M. Okumura. Extraction
and Visualization of Trend Information from
Newspaper Articles and Blogs. In Proceedings of
the 6th NTCIR Workshop, pp.414-419, 2007.
S. B. Needleman and C.D. Wunsch. A General
Method Applicable to the Search for Similarities in
the Amino Acid Sequence of Two Proteins. Journalof Molecular Biology, Vol. 48, pp.443-453, 1970.

TopicGasoline(T2N0101)

Personal Computer(T2N0102)
Communication
Device(T2N0103)
Political Trend(T2N0104)

Industry(T2N0105)

I-mode(T2N0106)

Digital Camera(T2N0107)

Center Exam
(T2N0108)

Total

Table 2. Evaluation results for each system
SystemPrecision
Recall
HCU1
0.13 (1/8)
0.03(1/33)
HCU2
0.38 (3/8)
0.09 (3/33)
HCU3
0.10 (1/10)
0.03 (1/33)
HCU4
0.30 (3/10)
0.09 (3/33)
HCU1
0.00 (0/0)
0.00 (0/19)
HCU2
0.00 (0/0)
0.00 (0/19)
HCU3
0.00 (0/3)
0.00 (0/19)
HCU4
0.33 (3/9)
0.16 (3/19)
HCU1
0.36 (4/11)
0.08 (4/50)
HCU20.62 (8/13)
0.16 (8/50)
HCU3
0.24 (5/21)
0.10 (5/50)
HCU40.27 (4/15)
0.08 (4/50)
HCU1
0.00 (0/0)
0.00 (0/85)
HCU2
0.00 (0/0)
0.00 (0/85)
HCU3
0.12 (22/181) 0.26 (22/85)
HCU40.13 (24/181) 0.28 (24/85)
HCU1
0.00 (0/0)
0.00 (0/27)
HCU2
0.00 (0/0)
0.00 (0/27)
HCU3
0.00 (0/0)
0.00 (0/27)
HCU4
0.00 (0/0)
0.00 (0/27)
HCU1
0.25 (1/4)
0.02 (1/45)
HCU20.75 (3/4)
0.07 (3/45)
HCU30.06 (1/17)
0.02 (1/45)
HCU4
0.24 (4/17)
0.08 (4/45)
HCU1
1.00 (1/1)
0.06 (1/18)
HCU2
1.00 (1/1)
0.06 (1/18)
HCU3
0.33 (2/6)
0.11 (2/18)
HCU40.50 (3/6)
0.17 (3/18)
HCU1
0.67 (6/9)
0.16 (6/37)
HCU2
0.78 (7/9)
0.19 (7/37)
HCU3
0.38 (6/16)
0.16 (6/37)
HCU4
0.63 (10/16)
0.27 (10/37)
HCU1
0.371
0.041
HCU2
0.629
0.070
HCU3
0.146
0.118
HCU4
0.197
0.159

F-measure
0.05
0.15
0.05
0.14
0.00
0.00
0.00
0.22
0.13
0.25
0.14
0.12
0.00
0.00
0.16
0.18
0.00
0.00
0.00
0.00
0.04
0.13
0.03
0.12
0.11
0.11
0.17
0.25
0.26
0.31
0.23
0.38
0.074
0.126
0.130
0.176