Trend Survey on Japanese Natural Language Processing Studies
over the Last Decade
Masaki Murata
†
, Koji Ichii
‡
, Qing Ma
†,§
, Tamotsu Shirado
†
,
Toshiyuki Kanamaru
†,∗
, and Hitoshi Isahara
†
†
National Institute of Information and Communications Technology
3-5 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0289, Japan
{murata,qma,shirado,kanamaru,isahara}@nict.go.jp
‡
Port and Airport Research Institute
Nagase 3-1-1, Yokosuka, Kanagawa 239-0826, Japan, ichii@pari.go.jp
§
Ryukoku University, Otsu 520-2194, Japan, qma@math.ryukoku.ac.jp
∗
Kyoto University, Yoshida-Nihonmatsu, Sakyo, Kyoto 606-8501, Japan
kanamaru@hi.h.kyoto-u.ac.jp
Abstract
Using natural language processing, we
carried out a trend survey on Japanese
natural language processing studies that
have been done over the last ten years.
We determined the changes in the num-
ber of papers published for each re-
search organization and on each re-
search area as well as the relationship
between research organizations and re-
search areas. This paper is useful
for both recognizing trends in Japanese
NLP and constructing a method of sup-
porting trend surveys using NLP.
1 Introduction
We conducted a trend survey on Japanese nat-
ural language processing studies that have been
done over the last ten years. We used biblio-
graphic information from journal papers and an-
nual conference papers of the Association for
Natural Language Processing, Japan (The Asso-
ciation for Natural Language Processing, 1995-
2004; The Association for Natural Language Pro-
cessing, 1994-2003). Just ten years have passed
since the association was established. Therefore,
we can use the bibliographic information from the
past ten years. In this study, we investigated what
kinds of studies have been presented in journal
papers and annual conference papers on the Asso-
ciation for Natural Language Processing, Japan.
We first digitized documents listed in the bibli-
ographic information and then extracted various
pieces of useful information for the trend survey.
Figure 1: Change in the number of papers
We also examined the changes in the number of
papers put up by each Japanese research orga-
nization and the changes in the number of pa-
pers written on specific research areas. More-
over, we examined the relationship between each
Japanese research organization and each research
area. This study is useful for trend surveys of
studies performed by members of in the Associa-
tion for Natural Language Processing, Japan.
2 Trend survey on NLP research studies
We show the changes in the number of journal
papers and conference papers in Figure 1. Jour-
nal papers are reviewed, but conference papers are
not reviewed in the association. In comparing the
journal papers and conference papers, we found
that the number of conference papers was much
larger than that of journal papers. We also found
that although both types of papers decreased in
number at some point, they both demonstrate an
upward trend.
Conference papers have a temporal peak in the
fourth year and a temporal drop in the sixth year,
250
Figure 2: Change in the number of journal papers
by each research organization (The two numbers in
the parentheses indicate the total number of papers
and the average value of published years.)
while journal papers have a peak in the sixth year
and a drop in the eighth year. The temporal peak
and drop of the journal papers occurred just two
years after the peak and drop of the conference
papers. We presume this is because journal papers
need more time for reviewing and publishing, and
because journal papers are presented later than
conference papers for studies performed at the
same time.
3 Trend survey on research
organizations
Next, we investigated the change in the number
of papers put out by each research organization.
The results are represented in contour in Figures
2 and 3. The height in contour (the depth of a
black color) indicates the number of papers. We
calculated the average (we call it average value)
of the average, the mode, and the median of the
published years by using the data of the number
of papers performed by each research organiza-
tion. In the figures, each research organization is
listed in ascending order of the average value. We
added the total number of papers and the average
value to each research organization in the figures.
Therefore, research organizations that had many
papers in the earlier years are displayed higher
on the list, while research organizations that had
Figure 3: Change in the number of conference pa-
pers by each research organization
many papers in the later years are displayed lower.
Here, we displayed only research organizations
that had many total papers. If a research orga-
nization’s name was changed during the ten-year
period, we used the name that had the most usage
on published papers for displaying it.
1
From these figures, we can see that ATR and
CRL (NICT) put out many journal papers, and
NTT, ATR, Tokyo Institute of Technology, CRL,
and the University of Tokyo put out many confer-
ence papers. We also found that while NTT and
ATR had many papers in the earlier years, CRL
and the Univ. of Tokyo had many papers in the
later years. We can expect that because CRL and
the Univ. of Tokyo demonstrate an upward ten-
dency, their quantity of papers will continue to in-
crease in the future. Using these figures, we can
see very easily in which reference year each re-
search organization put out many papers.
4 Trend survey on research areas
Next, we investigated the change in the number
of papers in each research area. The results are in
Figures 4, 5, and 6. (Because the volume of data
for conference papers was large, it was divided
into two figures.). For journal papers, the height
1
When we counted the frequency of a research organiza-
tion whose name was changed, we used all the names of it
including old and new names.
251
Figure 4: Change in the number of journal papers
in each research area
in contour indicates the number of papers. For
conference papers, the height in contour indicates
the base two logarithm of the number of papers
added by one. Using the same method as that de-
scribed above, we calculated the average of the
average, mode, and median of the years papers
were published using the data of the number of
papers in each research area. In the figures, each
research area is displayed in ascending order of
the average value. We added the total number of
papers and the average value to each research area
in the figures. Here, we divided the title of each
paper into words using ChaSen software (Mat-
sumoto et al., 1999), and we treaded each word as
a research area. A paper with a particular word in
Figure 5: Change in the number of conference pa-
pers in each research area (part I)
its title was categorized in the research area indi-
cated by the word. We manually eliminated words
that were not indicative of a research area, for ex-
ample, “teki” (of) and “kenkyu” (study).
From these figures, it is clear that the research
areas of “Japanese” and “analysis” were studied
in an especially large number of papers. We
also found that for journal papers, because the
research areas of “verb”, “noun”, “disambigua-
tion”, “probability”, “corpus”, and “polysemic”
were displayed higher on the list, these areas were
studied thoroughly in the earlier years. Likewise,
we found that the research areas of “morphol-
ogy”, “dependency”, “dialogue”, and “speech”
were studied thoroughly in the sixth year and the
252
Figure 6: Change in the number of conference pa-
pers in each research area (part II)
research areas of “summarization”, “retrieval”,
“translation” and so on were studied well in the
later years. Special journal issues on “summariza-
tion” were published in the sixth and ninth years,
so the research area of “summarization” was rep-
resented in many papers in those years. We can
expect that because the research area of “transla-
tion” demonstrates an upward tendency, the num-
ber of papers on this topic will continue to in-
crease in the future.
In terms of conference papers, we found that
the research areas of “bilingual”, “morphology”,
“probability”, “dictionary”, “statistics”, and so on
were studied well in the earlier years. In the lower
part of the figures, such research areas as “re-
Figure 7: Change in the number of conference pa-
pers at each research organization in the research
area of “translation”
Figure 8: Change in the number of conference pa-
pers in each research area in the research area of
“translation”
trieval”, “summarization”, “question” and “para-
phrase” are found. Thus, we can see that these
research areas were studied thoroughly in recent
years. We can see very easily in which reference
years each research area was studied using these
figures.
5 Trend survey using part of data
Although we have focused on using all the data
in the trend survey so far, we can narrow down
the survey by looking only at a certain part of
the data. For example, when we want to exam-
253
Figure 9: Relationship between research organizations and research areas in journal papers (The name
of each research organization is given a “� ” symbol.)
ine a trend survey on translation in more detail,
all we have to do is to extract papers on transla-
tion and use them for a trend survey. We carried
out a trend survey on machine translation in this
manner. We first extracted papers whose titles in-
cluded the word “translation” and then performed
the same investigations as in Sections 3 and 4.
The results are in Figures 7 and 8. The height in
contour (the depth of a color) indicates the num-
ber of papers. From Figure 7, we can see that
NTT had many papers in the earlier years, and
ATR had many papers in later years. From Figure
7, we can also see that studies on translation of-
ten dealt with specific topics such as “semantics”,
“knowledge” and “dictionary” in earlier years and
“support”, “example”, and “retrieval” in more re-
cent years.
6 Relationship between research
organizations and research topics
Finally, we investigated the various research ar-
eas that research organizations studied more fre-
quently during the ten-year period. Here, we
show only the results for journal papers. We used
the same method as in the previous sections for
extracting research organizations and research ar-
eas from the data. We counted the cooccurrent
frequency of each research organization and each
research area. We then constructed a cross table
in this manner and then performed the dual scal-
ing method (Weller and Romney, 1990; Ueda et
al., 2003). The result is depicted in Figure 9. The
dual scaling method displays the relationship be-
tween research organizations and research areas.
In Figure 9, “translation” appears in the lower
left quadrant, “learning” appears in the lower
right quadrant, “statistics” and “retrieval” appear
in the upper right quadrant, and “noun” and “sen-
tence” appear in the upper left quadrant. In the
vicinity around these words, the research areas
and organizations relating to them appear. For ex-
ample, in the upper right quadrant, Hitachi and
University of Tokushima appear near “statistics”
and “retrieval”, which were frequent study topics
for them. Similarly, “summarization” appears in
the near upper right area of the source origin and
is surrounded by JAIST, Toyohashi University of
Technology, and Tokyo Institute of Technology.,
indicating it was a frequent topic of study at those
institutions. We can easily see which research
topics were primarily studied by each organiza-
tion using this figure.
Also in Figure 9, research areas on numeri-
cals such as “probability” and “learning” appear
254
on the right side. Therefore, we can interpret the
figure as depicting quantitative research topics on
the right side and qualitative research topics on
the left side. Research areas using complicated
processing such as “learning” and “translation”
appear in the lower area and research areas deal-
ing with theory such as “probability”, “grammar”,
“sentence”, and “noun” appear in the upper area.
Therefore, we can interpret the figure as depict-
ing theoretical research topics in the upper area
and research topics using complicated processing
in the lower area.
7 Conclusion
In this paper, we described a trend survey carried
out on Japanese natural language processing stud-
ies done over the last ten years. We were able to
investigate trend surveys on research areas very
easily by treating divided words in titles by a mor-
phological analyzer as the indications of research
areas. We displayed the changes in the number of
papers put out by each research organization and
written on specific research topics. We also dis-
played the relationship between research organi-
zations and research areas using the dual scaling
method. The simple methods we used that are de-
scribed here made it possible to show many useful
results.
This paper has the following two significant ef-
fects:
 This paper explained a trend survey on
Japanese natural language processing. By
reading it, we can understand the trends in
research on Japanese natural language pro-
cessing. For example, we can find out
which research areas were studied more of-
ten and we can see which research organiza-
tions were involved in studying natural lan-
guage processing. We can also see which re-
search organization studied a particular re-
search area most often over the ten-year pe-
riod.
 We used natural language processing to
carry out the trend survey described here.
For example, we automatically detected the
indication of a research area from words
used in titles by using a morphological ana-
lyzer. In addition, we displayed words that
were extracted by the morphological ana-
lyzer in several ways to display the results
of the trend survey effectively. The methods
used in this paper would be useful in other
trend surveys.
In short, this paper is useful for recognizing trends
in Japanese NLP and for constructing methods of
supporting trend surveys using NLP.
In the future, we would like to perform an in-
ternational trend survey on natural language pro-
cessing using international conference and jour-
nal papers such as IJCNLP, ACL, and the Journal
of Computational Linguistics. We would also like
to do trend surveys on other topics such as AI, bi-
ology, politics, and sociology.
The kinds of investigations we did can easily be
altered to do many other kinds of investigations
as well. For example, we can use the dual scal-
ing method by investigating the relationship be-
tween the reference years and the research organi-
zations/areas. We can also use the representation
in contour for the relationship between research
organizations and research areas. Although we
showed the data in ascending order of the aver-
age value of the published years, we could show
the data in different order, for example, the or-
der of the total number of papers or the order of
the location, i.e., showing similar research orga-
nizations/areas that are located near each other by
clustering research organizations/areas using their
cooccurrent words. We would like to continue
to study these kinds of support methods for trend
surveys in the future.
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