J. Plasma Fusion Res. SERIES, Vol.7 (2006) 348-351

Design and Implementation of an Evolutional Data Collecting System
for the Atomic and Molecular Databases
SASAKI Akira, JOE Kazuki1 , KASHIWAGI Hiroe, WATANABE Chiemi1 , SUZUKI Manabu2 ,PICHL Lukas2 , OHISHI Masatoshi3 , KATO Daiji4 , KATO Masatoshi4 and KATO Takako4
Advanced Photon Research Center, Japan Atomic Energy Research Institute∗ ,1 Nara Women’s University, 2 The University of Aizu,
3 National Astronomical Observatory of Japan, 4 National Institute for Fusion Science（Received: 21 October 2004 / Accepted: 21 September 2005）

AbstractWe study an evolutional system to assist an individual to collect articles, which contain atomic and molecular data, and to develop the database automatically. A text classification technique based on LVQ (LearningVector Quantization) is proposed and its performance is evaluated using abstracts from atomic and molecularbibliographic databases as training and test samples.
Keywords:
atomic data, database, neural computing, learning vector quantization
1．Introduction
Increasing demand from basic science and industrial application requires collection and evaluation of alarge amount of atomic and molecular data [1]. Present
database development relies on manual collection of articles including the atomic and molecular data, and extraction of data. In recent times, papers in major journals have been published electronically and have been
made available on-line. Therefore, it would be possible
to develop computer software to collect articles automatically. The text classification approach will be useful to decide whether atomic data is included in the target article [2]. In this paper, we present the preliminary
results of LVQ (Learning Vector Quantization [3,4]),
which categorizes the paper in terms of the existenceof atomic and molecular data.

gories of (I) and (II) are equivalent, (II) is updated sothat it moves closer to (I). On the other hand, when thecategories are diﬀerent, (II) is updated so that it is kept
away from (I). The update is performed as follows.
In the case of identical category,W(new) = W(old) + α(X − W(old)).
In the case of diﬀerent categories,W(new) = W(old) − α(X − W(old)),
where W, X, and α are reference vector, feature vector oftraining samples, and learning factor, respectively. The
learning of LVQ is carried out by repeating the updateof the reference vectors with training samples.
3．Paper classification by LVQ
3.1 Process overview

2．Learning vector quantization (LVQ)
LVQ is a well-known supervised learning algorithm
for pattern recognition. The learning is performed withpre-categorized training samples, and the resultant LVQ
based system recognizes (classifies) unknown samples.
The learning uses a set of reference vectors generated
from several feature vectors. A feature vector represents
the characteristics of a training sample. The initialization method for the reference vectors does not aﬀect thelearning process significantly.
Given a training sample, the nearest reference vector(II) to a feature vector (I) is selected. When the cate-

Our paper classification process consists of following steps.
Step 1）Preparation of training data (papers and theirabstracts) and test data (abstracts, (3)). Classification of the training data into (1) the abstracts containing atomic and molecular dataand (2) the others. We refer to category-1 as(1) and category-0 as (2).
Step 2）Pre-process (1), (2) and (3). The pre-processgenerates feature vectors with term frequency
information.
C 2006 by The Japan Society of Plasma

Corresponding author’s e-mail: sasaki.akira@jaea.go.jp∗ Present: Japan Atomic Energy Agency

Science and Nuclear Fusion Research348

Design and Implementation of an Evolutional Data Collecting System for the Atomic and Molecular Databases

3.3 Feature vector

We use the frequency of each word in the abstractsof the target samples for the feature vector of LVQ.
The frequency is calculated by the TF/IDF (Term Frequency/Inverse Document Frequency) method [6]. TheTF/IDF method is employed to measure the importanceof a word used in a document on the basis of its appearance frequency, and in general assigns a weight to theword.
3.4 Learning with LVQ

The learning with LVQ is performed until the recognition rate for the training samples reaches 97 %. In thecase that the recognition rate does not reach 97 % withintwenty epochs, the learning is forced to stop. This is because the recognition rate converges within 20 epochs
for the most cases. In our experiments, the learning
factor is valid unless it exceeds 0.5. We adopt a conservative value of 0.2 through all the experiments. Eachreference vector is obtained as an average vector of randomly selected five feature vectors for training samples
on the basis of the category. This method of definingreference vectors is employed to reduce the learning
cost.

Fig. 1 System overview

Step 3）Apply the LVQ algorithm with the feature vectors of (1) and (2).
Step 4）Apply the learned reference vectors with thefeature vectors of (3) for the recognition of theabstracts of atomic data and molecular data related papers.
3.2 Pre process
3.2.1 Extract abstracts

Abstracts downloaded from on-line journals are aform of html files. We need to extract the text datafrom the html files for the LVQ learning process. In
this paper, we use abstracts of six on-line journals, eachof which have a diﬀerent format. Hence, we develop aparser that understands the six diﬀerent formats in order to detect and extract the text regions. In order todevelop the parser, we make use of a freeware tool Flex[5] for lexical analysis.

4．Evaluation
In order to validate our paper classification system,
we evaluate the prototype using several experiments.
The test data for the experiments is selected from abstracts of 364 physics papers [7]. Each experimentshows the rates of correctness, reproduction, and accuracy.
The correctness rate is the probability that a given
test sample is classified into the correct category. The
reproduction rate shows the probability that a given test
sample, which is an abstract of a paper for atomic andmolecular data, is classified into category-1. The accuracy rate is the ratio of the number of test samples,
which are known to be in category-1, to the number oftest samples, which are classified into category-1 by ourprototype.
A trade-oﬀ relation exists between the accuracy of
the reproduction rates. When the reproduction rate isimproved, the number of test samples that are classified
into category-1 also increases. This leads to a deterioration in the accuracy rate. On the other hand, improvement in the accuracy rate causes a degradation of thereproduction rate because the number of test samples,
which are removed from category-1, increases. Therefore, we should clearly determine which rate we should
focus on.
At this point, we are interested in the robust classi-

3.2.2 Chemical symbols and mathematical expressions

Chemical symbols and mathematical expressions arecommonly used in physics papers. Since they often
include unexpected blanks or special fonts, detecting
meaningful regions of the text using a parser is at times
very diﬃcult. For such chemical and mathematical expressions, we adopt two types of tags to be inserted byusing Flex: math /math for a mathematical expression and chem /chem for a chemical symbol. Withthe help of the tags, the parser can understand the abstract text. In this paper, we do not use chemical symbols and mathematical expressions for constructing feature vectors because we focus on the text classificationand not on the special keyword detection. All the otherwords are processed with stopword removing and stemming, which are commonly used for text processing.

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Design and Implementation of an Evolutional Data Collecting System for the Atomic and Molecular Databases

fication wherein any test sample of category-1 is classified as belonging to category-1 with certainty rather
than an inaccurate classification wherein any test sample of category-0 is classified into category-1. At
this point, we are interested in the robust classification
where any test sample of category-1 is surely classified into category-1 rather than the accurate classification where any test sample of category-0 should not beclassified into category-1. Hence, we focus on the reproduction rate.
In this experiment, we use 364 abstracts: 182 as
training samples and 182 as test samples. The feature
vector consists of 1,140 elements.

Fig. 4 Accuracy rate and learning epochs

training samples does not aﬀect the variation of the correctness rate significantly. In the meantime, Fig. 3 and 4
show the reproduction and the accuracy rates are in therange of 55 % − 80 %. The amount of the variation cannot be ignored. We expect that the large variation can be
reduced by increasing the number of training samples.

4.1 Experiment 1

In order to investigate the change of the correctness, reproduction, and accuracy rates on the basis of
the selection of the training samples, the LVQ learning is carried out with various sets of training samples(Experiment-1). The number of reference vectors is120, where 60 % are for category-1, and 40 % are forcategory-0. Figures 2 4 show the correctness, reproduction, and accuracy rates, respectively.
Figure 2 shows that the correctness rate is in therange of 65 % − 75 %. In particular, the selection of

4.2 Experiment 2

Experiment-2 is conducted to study the optimal ratio of the number of reference vectors for category-1 tothat of all the reference vectors. In this experiment, thenumber of reference vectors is 120, and the ratio of thenumber of reference vectors for category-1 to that of all
the reference vectors varies from 10 % to 90 %. Figure
5 indicates the correctness, the reproduction, and theaccuracy rates for each ratio. Each rate is an averageof one hundred recognition results with diﬀerent LVQ
learning tasks.
We observe a higher reproduction rate as the number of category-1 reference vectors increases. The correctness and accuracy rates reach the maximum when
the ratio of category-1 reference vectors is 30 %. With

Fig. 2 Correctness rate and learning epochs

Fig. 5 Correctness, reproduction, and accuracy rates obtained by varying the ratio of the number of reference vectors for category-1 to all the referencevectors

Fig. 3 Reproduction rate and learning epochs

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Design and Implementation of an Evolutional Data Collecting System for the Atomic and Molecular Databases

more category-1 reference vectors, both the correctness
and the accuracy rates decrease gradually.
As discussed above, we are of the opinion that thereproduction rate is more important than the accuracy
rate. When the reproduction rate is at its highest, i.e.,
when the ratio of category-1 reference vectors is 90 %,the correctness and the accuracy rates are 69.94 % and63.82 %, respectively. Both rates are too low to be accepted. Considering the combination of the three rates,
we adopt 50 % as the optimal ratio of category-1 reference vectors.

as the optimal number of reference vectors, and 50 % is
the optimal ratio of the number of category-1 reference
vectors to that of all the reference vectors. Furthermore,
we observe that the selection of training samples does
not aﬀect the variation of the correctness rate significantly while the reproduction and accuracy rates, between which a trade-oﬀ relation exists, vary relatively.
Another experiment with an optimal number of reference vectors and optimal ratio of catgegory-1 reference vectors results in the correctness rate of 70.52 %,reproduction rate of 65.88 %, and the accuracy rate of
65.21 %. Although the system appears to be unsuitablefor practical application on the basis of its performance,
we have identified a number of areas for improvement.
First, we do not take into account the chemical andmathematical expressions, which are typically considered to be the most important information to classify thegiven abstracts, for our prototype. Second, we merely
adopt the basic LVQ for our prototype. Other extensions of LVQ algorithms can be used for our prototype.
Finally, we may reconstruct the architecture of featurevectors. The feature vector should be built from a larger
database pertaining to atomic and molecular data.

4.3 Experiment 3

Experiment-3 is conducted to investigate the optimal
number of reference vectors. The correctness, reproduction, and accuracy rates are examined by varying thenumber of reference vectors from 10 to 350. Figure 6
shows those rates and the various number of referencevectors. Each rate is calculated as an average of one
hundred recognition results with diﬀerent LVQ learning tasks. It should be noted that the ratio of category-1
reference vectors is 50 %.
As the number of reference vectors increases, thecorrectness and accuracy rates decrease while the reproduction rate increases gradually. After the numberof reference vectors reaches 270, each rate does notchange significantly. When the number of reference
vectors is 270, the correctness and accuracy rates are
68.99 % and 68.39 %, respectively. Since we are of theopinion that the correctness rate should at least be 70 %,the correctness rate of 68.99 % is not acceptable. With
these restrictions, we observe that the number of reference vectors should have an optimal value of 110.

Acknowledgements
The author (A.S.) acknowledges useful discussions
with Drs. H. Kubo, H. Ihara, Y. Ueshima, and K.
Moribayashi from JAERI and Dr. M. Murata from theNational Institute of Information and Communications
Technology.

[1]
[2]

4.4 Evaluation summary

Performing several experiments, we observe that 110

[3]
[4]

[5]
[6]

[7]
Fig. 6 Correctness, reproduction, and accuracy rates obtained by varying the ratio of the number of reference vectors

351

References
Atomic and Molecular Data Research Center,NIFS, http://dpc.nifs.ac.jp/admrc/index-j.html.
S. Gao, W. Wu, Chin-Hui Lee, Tat-Seng Chua,”Maximal figure-of-merit learning approach totext categorization”, ACM SIGIR, p.174-181.
(2003).
T. Kohonen ; The Self-Organizing Maps (3rd edition), Springer, 2001.
HUT - CIS - Research - SOM PAK, LVQ PAK,
http://www.cis.hut.fi/research/som-research/nnrcprograms.shtml
Flex GNU Project Free Software Foundation(FSF), http://www.gnu.org/software/flex/.
G. Salton and M.J. McGill : Introduction to Modern Information Retrieval, McGraw-Hill BookCompany, 1983.
Positive samples are taken from the list created byProf. Y. Itikawa, a part of the list is published inADANDT, 63, 315 (1996). Negative samples aretaken from Phys. Rev. A-E, vol. 69, No.1.