The Company They Keep:
Extracting Japanese Neologisms Using Language Patterns
James Breen
University of Melbourne
Melbourne, Australia
jimbreen@gmail.com

Timothy Baldwin
University of Melbourne
Melbourne, Australia
tb@ldwin.net

Abstract
We describe an investigation into theidentification and extraction of unrecorded potential lexical items inJapanese text by detecting text passages containing selected language patterns typically associated with suchitems. We identified a set of suitable
patterns, then tested them with twolarge collections of text drawn from the
WWW and Twitter. Samples of theextracted items were evaluated, and it
was demonstrated that the approach
has considerable potential for identifying terms for later lexicographic analysis.

1 Introduction
As the coverage of lexicons (including wordnets) improves, deciding which words should
be added next becomes an issue. New words
are constantly being added to languages, andexisting words are not always covered by current lexical resources.
This paper reports on an investigation as
to whether it is possible to identify and extract neologisms (newly created words and expressions) from Japanese text based on thelanguage patterns in which they occur. The
genesis of the project is the observation that
one often encounters in Japanese text terms
which the writer thinks needs some explanation, either because they are new or uncommon. This may be signalled by following theterm with phrases such as というのは (to iu no
wa “as for that which is said ⟨term⟩”) and とは(to wa “as for ⟨term⟩”), sometimes combinedwith the reading in parentheses, and then followed by an explanation. The phenomenon iswell known to Japanese translators, who often

Francis Bond
Nanyang Technological
University, Singapore
bond@ieee.org

will do a WWW search for “⟨term⟩ とは”, etc.
when encountering an unfamiliar term in order to identify cases where the term is being
described, discussed or otherwise highlighted.
The investigation broadly breaks into two
components:a. the identification of the sorts of language
patterns used to describe, discuss, highlight, etc. terms;
b. the extraction and evaluation of terms sotargeted by those language patterns.

2 Prior Work
Research into the use of linguistic patterns
in text to detect terms of interest has takenplace in several contexts. In keyphrase extraction Hasan and Ng (2014) have produced awide-ranging survey of the various techniques
used in keyphrase extraction and their relative effectiveness, and Kim et al. (2013) evaluate the performance of a variety of supervisedand unsupervised approaches. In term extraction, which is a major part of the broader
field of terminology, usually in technical contexts (Kageura (2000)), Takeuchi et al. (2009)
adapted the French ACABIT system, which
detects morpho-syntactic sequences, to isolate terms in Japanese for later analysis. Le
et al. (2013) used patterns of phrases to identify particular Japanese legal documents of interest. Mathieu (2013) successfully adapted
a keyphrase extractor for use with Japanese,
although its use was restricted to kanji sequences. The relationship between a text pattern and a term of interest is a form of collocation, i.e. lying between idiomatic expressions and free word combinations. In theirsurvey of collocations in language processing,
McKeown and Radev (2000) explore the roleof the extraction of collocations in lexicography, although the focus is on the identification

of general terms rather than those which arehighlighted as being of interest. Prior published research into the use of Japanese text
patterns which target general terms of interest
appears to be quite limited. Sato and Kaide(2010) employed a related technique for extracting English–Japanese name pairs by scanning texts for nearby occurrences of Mr, Mrs,
etc. and the Japanese equivalents, e.g. さん(san).

3

Text Corpora

An essential element of the investigation isthe availability of substantial quantities ofJapanese text, preferably from a variety ofsources. While there are number of Japanese
corpora available for use in NLP work, mostare actually quite small. In this study we used
two text collections:a. the Kyoto WWW Corpus. This is a collection of 500 million Japanese sentencescollected from WWW pages in 2004. The
main problem is that it is getting dated,and hence what may have been neologisms
at the time of its collation may well berecorded and accepted now, or have totally
faded from use.
b. Twitter text. We used a collection of
870 million Japanese text passages extracted from 2014 and 2015 Twitter data.
This data provides the opportunity to see
how the techniques under investigation perform with with contemporary and at times
slangy text.

4
4.1

Initial Exploration
Pattern Frequencies

Initially we explored whether the text patterns
typically associated with the discussion of particular terms occur in sufficient quantities tomake them useful search keys by examining
their frequencies in the Google Japanese ngram Corpus (Kudo and Kazawa, 2007) (seeTable 1).
The high-scoring とは is really a common
form of topic marker without any particularassociation with new or unusual terms, andalmost certainly would produce very noisy results if used as a search pattern. On the other
hand というのは, という言葉, という意味 and の
意味は are typically associated with particular

Term

Frequencyとは to wa “as for”というのは/と言うのは
to iu no wa “as for the said”という言葉/ということば
to iu kotoba “said term”という意味/といういみ
to iu imi “said term’smeaning”という意味は to iu imi wa“as for the said term’smeaning”の意味は/のいみは
no imi wa “as for themeaning of”

169,756,339
19,134,679/1,207,555
5,360,613/167,095
4,544,800/10,364
51,726
1,979,108/1,169

Table 1: Google n-gram Corpus Frequenciesof Text Patterns

terms and are probably worth further investigation.
4.2 Testing Contexts of Known New
Terms
We also investigated the sorts of contexts inwhich known new terms are being used tosee if any useful additional patterns could be
identified. As an initial exploration 5 terms
were chosen from recent additions to the JMdict database (Breen, 2004) which had been
noted as popular new words/expressions. The
5 terms were:• マタハラ matahara abbreviation meaning“workplace discrimination against pregnant women”;• こじらせ女子 kojirase joshi “girl who has
low self-esteem”;• ナマポ namapo slang for “welfare recipient”• 美魔女 bimajo “middle-aged woman wholooks very young for her age”
• 隠 れ メ タ ボ kakure metabo abbreviation
meaning “normal weight obesity”
10 sentences for each term were extractedusing a WWW search. While this is clearlya small number of samples, it emerged that
there were relatively few of the と い う/とは/etc. sorts of patterns used; only four occurred a total of seven times in the 50 sentences, and quite a number of the terms being
tested occurred encapsulated by some form ofparentheses, either “...” (5 occurrences), 「...」Term
造語 zōgo“neologism, coinage”
新語 shingo“neologism, new word”
現代用語 gendai yōgo“neologism, recent word”
新造語 shinzōgo“neologism, new coinage”
言語新作 gengo shinsaku“neologism (esp. medical)”
造語症 zōgoshō“neologism (esp. medical)”
ネオロジズム neorojizumu“neologism”
ネオレジズム neorejizumu“neologism”

Frequency
232,837
152,785
62,705cate the approach seems to have considerable
promise. Quite a few relatively new terms,
such as ブ ロ マ ン ス buromansu “bromance”,
were in the samples. It is also interesting
to note that all the terms referenced by thepatterns were encapsulated in some forms ofparentheses.

3,978
220
<20
<20
<20

Table 2: Google n-gram Frequencies for Words
Meaning NeologismTermという造語/と言う造語(to iu zōgo)という新語/と言う新語(to iu shingo)という現代用語/と言う現代用語(to iu gendaiyōgo)

Frequency
10042/491
3140/117
50/<20

Table 3: Google n-gram Frequencies for Extended Neologism Patterns

4.4 Parenthesized Kana
It has been observed that explanations ofterms in Japanese are often accompanied by
the reading of the term in parentheses.
To evaluate whether parenthesized readings
are present in association with the sorts of language patterns under consideration, and if sowhether they are in sufficient quantities to include them in the text analysis, a scan wasmade of the Kyoto Corpus to extract all sentences containing the patterns described above(という言葉, という造語, etc.). Approximately
2.4 million sentences were extracted, and thesewere analyzed to determine if they containedparenthesized strings of kana. Only 116 textlines contained “(kana)” patterns, and of these
there was only one passage containing the“term (reading)” pattern, which indicated that
this pattern was not common enough to makeit worth a lot of attention.
4.5 Expansion of Linguistic Patterns(10 occurrences) or 『...』 (1 occurrence).1
4.3

Explicit Neologism Labelling

We then investigated the use of terms inJapanese which can mean neologism, some ofwhich are given in Table 2, along with theirrelative frequencies from the Google n-grams.
As the first three account for almost all theusage, these were investigated further for theiruse in combination with the という and と言う(“as said”) patterns (Table 3).
As the frequencies for という造語 and という
新語 looked promising, a sample of 10 sentences for each was identified via a Google
WWW search. These sample sentences indi1
Japanese orthography uses a variety of symbols fortext encapsulation, with the 「」 pair commonly used
where inverted commas are used in English. Othersymbols used for this include: 〈〉, 《》, ＜＞, 〔〕, ［］ and 【】Discussions were held with several native
speakers of Japanese in order to identify possible patterns which may be used with newterms. From this a number of additional patterns were identified. Some also typically followed the term in question, e.g. xx という言葉を聞き to iu kotoba wo kiki “hearing the said
word xx” and xx という不思議な to iu fushigi
na “the said xx is strange/curious”.
In addition, a set of phrases which would
precede a target word was identified, e.g. この頃よく聞く xx kono goro yoku kiku, 近頃よく聞く xx chikagoro yoku kiku, and 最近はやりの xx saikin yoku kiku, all of which mean “theoften heard recently xx”.
This resulted in an overall set of 37 text patterns, some of which have alternative surface
forms, e.g. このごろ and この頃 (kono goro).

4.6

Initial Evaluation of the Language
Patterns

The 37 text patterns were tested against the
Kyoto WWW Corpus. For each pattern asample of 20 sentences was examined in detail,with each sentence being classified into one ofthree groups: sentences which did not directlydiscuss any identifiable word or term (1); sentences which focussed on a word or term whichis already established in one or more lexicons(2); and sentences which focussed on a wordor term which is not in an accessible lexicon,
and which warrants further investigation (3).
It was clear that some of the text patterns
were quite effective in identifying text passages
which focus on words or terms of interest, andin some cases the precision appeared to bequite high; in three of the sets of samples (という造語, という新語, という新しい言葉) all of
the passages had such a focus, and in anotherfive (という言葉を聞き, という言葉を耳に, という言葉が話題に, という言葉がはやって, という流行語) 85% or more had that focus.
Around half of the sampled passages (349)
were classified into Groups 2 and 3, and thesewere about evenly split between those where
the target term was in parentheses (177) andthose where it was not (172).
Overall the numbers of sentences extractedwith the selected patterns only made up avery small proportion of the sentences in the
Corpus. Of the approximately 500 million
sentences the high precision patterns only
extracted 2,600 sentences. When combinedwith lower precision patterns the numbers extracted came to about 280,000 (about 0.06%),
and it was observed that most of these werefrom one pattern (という言葉).

5

Detailed Investigation

From the original set of 37 patterns, a set of 18
were chosen for further experimentation. The
selection process was to choose those patterns
which had resulted in the higher proportion ofGroup 2/3 being detected in the sampling.
Excluded from the original set were threeof the more commonly occurring patterns: と言 う の は/と い う の は, と い う と and と い う
い み/と い う 意 味. Although between them
they accounted for about 80% of the of thesentence selections, they performed compara-tively poorly in being associated with possibly useful terms. Of the chosen patterns ということば/という言葉 accounted for over 90%of the remaining extracted lines, and 最近はやりの/最近流行の/最近流行りの accounted
for a further ∼7%. Thus the overwhelming
majority of remaining extractions come fromtwo patterns. They are among the middleranking performers according to the sampling,
and certainly cannot be ignored. While there
are other patterns which performed considerably better in the sampling in terms of precision, the number of actual extractions associated with them is much lower.
5.1 Text Scanning and Target Term
Extraction
With over a billion lines of text to examine
for the presence of the language patterns areasonably fast searching technique is desirable. The possibility of training a machinelearning model was considered, however since
we are dealing with a constrained set of patterns a direct pattern-matching approach isclearly more appropriate. Also the nature of
the patterns lends itself to a fast character-bycharacter search using a search tree. The patterns being used begin with only four different
characters: こ, と, 近 and 最, and initially eachcharacter in a line of text only has to be compared with them to determine whether moreof the tree is to be searched. Similarly at eachlevel of the tree only a few characters typicallyneed to be tested.
The 500 million lines in the Kyoto Corpus
had 280,574 matches with these patterns, andthe 870 million tweets had 130,310 matches.
The hit rate for these patterns in Twitteris thus only about 30% that of the WWW
text, which is probably indicative of both thebrevity of many tweets, and possibly a very
different text style for longer tweets.
From the extracted lines of text, it was necessary to isolate the target terms associatedwith the patterns. The approach taken was:a. divide the patterns into those where the target usually precedes the pattern (these always begin with という), and those where
the target usually follows (the rest).
b. detect and extract text which occurredin some form of parentheses before or after the pattern. The extraction was re-stricted to parenthesized terms beginning 3
or fewer characters before or after the pattern. This margin was to allow for the occasional punctuation characters and words
such as など nado “et cetera”. Also it was
clear that there were occasionally quite long
strings of parenthesized text, typically quotations, which were not going to be considered valid lexical items, so the extraction
was restricted to strings of up to 10 characters.
c. where there are no parenthesized target
strings associated with the text patterns,
it is necessary to attempt to extract target
terms from the text preceding or following
the patterns. Inspection of a number ofpassages indicated that most likely candidates were made up of combinations suchas noun–noun, prefix–noun, noun–suffix,
adverb–noun, adjective–noun, etc. and thata reasonable heuristic would be to collectmorphemes until one which typically lies on
the boundary of an expression, such as aparticle or a verb, was encountered.
To implement this approach, the text following or preceding the pattern was passed
through the MeCab morphological analyzer(Kudo et al., 2004; Kudo, 2008)2 operating with the Unidic morpheme lexicon(Den et al., 2007), and adjacent morphemes
which met a limited set of part-of-speech(POS) attributes were aggregated
For each text collection the target term extraction as described above was run, the extracted terms were filtered against a large reference lexicon (as the aim of the investigation
is to determine whether the method is extracting new or unrecorded terms), and the remaining unlexicalized extractions were sorted andaggregated to determine how often they occur.
This is to enable evaluation of the hypothesisthat more frequently-occurring terms are more
likely to be potential lexical items. The numbers of target terms extracted from the text
collections is shown in Table 4.
Some general observations that can be madeabout these extractions are:a. the extractions comprise a very small proportion of the text in the two collections.
The passages extracted from the WWW
2

http://taku910.github.io/mecab/

Corpus represent only 0.056% of the text
and the ones from the Twitter collection
only 0.015%.
b. the ということば/という言葉 pattern is relatively much more common in the Kyoto
Corpus (0.054%) than in the Twitter collection (0.013%). The 最近流行りの/etc.
pattern is also more common in the Kyoto
Corpus, but not to such a degree.
c. the target terms are clearly less likely to
be parenthesized in Twitter text, and also
the target terms associated with という...
patterns are more likely to be parenthesizedthan the others where the target follows thepattern.

6 Evaluation of Extracted Target
Terms
The extracted terms were then categorized according to the usefulness of the term as a lexical item. This involved examining the term
both in the context of the text passage(s) inwhich it was detected, in other text passages
such as those discovered from WWW searches,
and in reference material such as glossaries
which were not part of the reference lexicon.
From this categorization codes were assigned
to the terms as follows: (A) in the reference
dictionary in different surface form, e.g. partially or fully in kana instead of kanji; (B) an
inflected or variant form of existing entry; (C)definitely of interest as it has the potential to
be a valid lexical item; (D) other, e.g. a phrase
not of particular interest; (E) corrupted text.
Also recorded was whether the occurrencesof the terms were parenthesized or not, andwhich pattern(s) generated the extraction.
(This was done for the “C” terms.)
6.1 WWW Corpus
Of the 234,733 terms extracted from this Corpus, 68,644 were not in the reference lexicon. Of these 52,277 were terms that occurred
only once, and the remainder occurred multiple times (the maximum was 55 times).
A detailed analysis of 120 terms was carriedout as follows: the most common 50 terms(13–55 occurrences), a sample of 20 terms
which occurred 5 times each, and a sample of
50 terms which occurred once each. The categorization of the terms is shown in Table 5.

Source

Total lines

Extractions(Paren.)Extractions(Non-paren.)Noneextracted

WWW Corpus(all patterns)
Twitter(all patterns)
WWW Corpus(という言葉)Twitter(という言葉)WWW Corpus(最近流行りの)
Twitter(最近流行りの)WWW Corpus(the rest)
Twitter(the rest)

28057412437111036245841

130310

37083

71995

21232

27055312272710311144715

119871

36074

64254

19543

6711

573

5653

485

7635

314

6530

791

3310

1071

1598

641

2805

696

1211

898

Table 4: Target Term Extraction Counts

Category

Top 505 Times
(20)

Once
(50)

A
B
C
D
E

15
6
18
8
3

2
6
10
2
0

0
1
3
46
0

Table 5: Categorizations of Extracted Text —WWW Corpus
Some examples of the extractions are:(A) がんばれ ganbare: kana form of 頑張れ“go for it!”(A) ガイジン gaijin: katakana form of 外人“foreigner”(B) 愛している aishiteiru: from the verb 愛す
る and meaning “to be in love”(B) 感動した kandōshite — past tense of 感動する “to be moved”(C) ゲ ー ム 性 gēmusei “quality of a video
game; game rating”(C) 共 創 kyōsō “growing together; joint
development”(D) シンプルイズベスト shinpuru izu besuto(“Simple Is Best”: pop song name)

The relatively high proportion of “C” terms
in the multiply-occurring sets (36–50%) is interesting. It might seem intuitively obviousthat more commonly used or discussed terms
would be more likely to be potential lexical
items, but it could well not have been the case.
More sampling of the 2, 3 and 4 batches may
be appropriate, but it seems clear that multiple occurrences of a term, at least among theterms extracted here, is a signal of its likelihood to be of interest.
6.2 Significance of Multiple
Occurrences
It was noted that the three singly-occurring C
extractions in Table 5 all had reasonably highcounts of occurrences in the n-gram Corpus(258–473). That raises the question of whether
the number of Corpus occurrences is linked
or correlated to the usefulness of extracted
terms. To test this a sample of 10 of thesingly-occurring “D” terms was checked to determine the number of occurrences in the Corpus. 6 of these occurred fewer than 10 times
and the others occurred 39, 52, 62 and 1,561
times respectively. Also checked were the Corpus counts of the 8 “D” terms in the “top 50”
set. While they varied, they were noticeably
lower than the “C” counts. This seems to indi-cate support for a (quite reasonable) hypothesis that low overall occurrence counts are related to the usefulness of extracted terms.
As a further test of this hypothesis, a set of
2,000 of the singly-extracted terms was chosen
and their overall counts in the Corpus established. About 160 of these (8%) each occurred
400 or more times. Examination of a sample
of 20 of these more commonly occurring terms
resulted in the following category counts: B:
1, C: 14, D: 6.
This is a very different outcome to thatshown by the randomly selected singlyextracted terms, and it seems likely that a highextraction count and/or a high overall Corpus
count are good indicators that an extracted
term has has a chance of being a term of interest. The overall Corpus count of a term may
not be a particularly useful metric as it would
be difficult to obtain in a general harvestingprocess. They are only available with the Kyoto WWW Corpus because an n-gram corpus
and associated utility software are available.
However a useful corpus count could well betaken from a different comprehensive corpus
such as the Google n-gram Corpus.

layed by Twitter users may in itself be usefulin the analysis of the passage.
The actual identification of re-tweets has
proved to be a significant problem as we observed that often the users make minor amendments before sending the message as though it
were new; often such relays of modified tweets
outnumbered the formal re-tweets.
6.5 Analysis of Re-tweets

A similar analysis was carried out on the textof 2014/15 Twitter data. Some additional
analysis was carried out on two aspects of thisdata: where the text passages were identified
as “re-tweets” these were aggregated and aseparate investigation made of the term to see
if occurrence within a re-tweet was any different to other target terms in terms of usefulness; and since the Twitter text was associated with specific dates, an analysis was madeto determine if identified terms were clusteredand if so whether this was associated withgreater usefulness.

The terms extracted from re-tweets were aggregated and ranked according to the numbersof times the tweet was repeated in order to see
if greater repetition was associated with theusefulness of the extracted term. Samples ofterms from the over 100 repetitions, 10 to 99
repetitions and 5 repetitions groups were selected and examined. From this examination
it was concluded that the occurrence of extracted terms in re-tweets was not a strong
indication of usefulness.
A similar investigation was made of a sample of multiply-occurring terms that were notin re-tweets, and as with the investigation of
the extractions from the WWW Corpus discussed above, it does appear that the numberof times a term is extracted is correlated withthe likelihood it is of interest.
As with the WWW Corpus terms, a sample of singly-occurring terms was checkedagainst an n-gram corpus, in this case theGoogle n-gram Corpus. A selection of 2,000
singly-extracted candidate terms was matchedagainst the Corpus and a sample of 20 of thehigher-ranking terms was evaluated. The results were 7 terms ranked as A or B, 5 as C and
8 as D. While this is only a small sample, itdoes seem to indicate that a high count in an
n-gram Corpus indicates a greater likelihoodthat a term is of interest.

6.4

6.6 Classification of Names

6.3

Twitter Data

Re-tweets

The fact that Twitter text contains “retweets”, i.e. messages repeated by Twitterusers to their followers, raises a number of issues in terms of the analysis of the text. On
the one hand the re-tweeting can seriously distort any analysis which attempts to use frequency information with regard to such things
as extracted terms (Lu et al., 2014). On theother hand the fact that a passage is being re-

In contrast to the terms identified in the
WWW Corpus, a significant proportion of
the terms extracted from Twitter text werenames, e.g. anime characters, Pokemon characters, singers, etc. In hindsight there probably should have been a category for them, as
they have been treated as “D” (not of interest). The fact they are being collected is anindication of the efficacy of the approach.

6.7

Issue of Parenthesized Terms

As previously described, the method for extracting possible terms involves either collecting a string of text in parentheses associatedwith the pattern, or collecting a string of morphemes with restricted POSs associated withthe pattern. It is worth examining the relative outcomes of these two approaches to determine if there is a qualitative difference.
Of the approximately 27,000 potential terms
extracted from the Twitter text, 12,650 wereparenthesized and 14,348 were not parenthesized. Samples were selected from the twogroups of terms and examined in detail. From
this it was determined that there is no clear
domination of one approach over the other.
6.8

Burstiness

As the Twitter texts have dates in their metadata it was possible to examine whether multiple occurrences were in bursts, and whether
this might be associated with greater or lesser
relevance.
A sample of ten non-re-tweet
multiply-occurring extractions ranging from
16 to 48 occurrences was examined. Of the 10,
3 were clustered into a relatively short period,e.g. a few days, and the other 7 were spreadover the whole period of the data. From this isdoes not appear that clustered multiple occurrences of candidate terms have any particularadvantages. The clustering may indicate a degree of topicality of a term, although it may
lead to focus on an ephemeral term, when agreater spread of usage over time may indicate
more general usage.

7

Precision and Recall

The establishment of precision and recall metrics in this area poses an interesting challenge. In terms of precision the testing reported above indicates that some patterns, e.g.という造語/という新語, are likely to result in
fairly high levels, however if they result in a
relatively small number of lexical items being
collected it is of limited use in lexicon building. Casting a wider net and being preparedto sift results is probably a better course.
In terms of measuring recall the typical approach would be to identify how many termsof-interest there are in a corpus, and test how
often they are identified by the extraction

method. To probe this issue the 10 candidate terms examined in Section 6.8 above were
tested to see how often they occurred in thetext, both in and out of the extraction patterns.
In 8 of the 10 terms over half of the occurrences in the Twitter text had been identified,and in three cases over 95% were identified.
The proportions identified in the WWW Corpus were noticeably lower.

8 Discussion and Conclusions
From the investigations described above, anumber of conclusions can be drawn and observations made about the techniques being investigated. Among them are:a. it is clear that the technique is quite effective in highlighting terms suitable forfurther investigation, as it identifies candidates that are often very worthy of detailed
examination and subsequent lexicalization.
b. it is interesting and not a little frustratingthat after all the early work in identifying
useful text patterns for identifying possible terms, the outcome has been so totally
dominated by two patterns, to the extent
that the others may as well be ignored. Several of the other text patterns have demonstrably better precision, but their recall ofuseful terms is so low as to make them oflittle use in a practical harvesting exercise.
(That is no reason, of course, to exclude
them as they add little overhead to process and at the margin can improve the outcome.)c. the technique can clearly be enhanced byassociation with an n-gram corpus with frequency counts. A term, particularly one
which has not been extracted often, is much
more likely to be a useful candidate if it has
a high n-gram count.
d. at present we have no real indication of therecall of the techniques being investigated.
Objective analysis of recall would be a major task and best left for further work.
e. one could envisage this technique being attached to something like a Twitter feed, andpassing extracted candidate terms througha frequency and n-gram analysis, and ultimately on to lexicographers for analysis.

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