Identification of Neologisms in Japanese Corpora using Synthesis
James Breen1, Timothy Baldwin2, Francis Bond3
1
Monash University, Melbourne, Australia
2
University of Melbourne, Melbourne, Australia
3
Nanyang Technological University, Singapore
Abstract
We report on the investigation of a neologism detection approach involving the synthesis of possible
Japanese words by mimicking Japanese morphological processes, followed by testing for the presence ofcandidate words in Japanese corpora. A 2-kanji compound generation and classification technique resulted in
the detection of significant numbers of unrecorded terms.
Keywords: Japanese, term synthesis; n-gram corpora, neology
1. Introduction
This paper reports on part of a major study into the extraction of neologisms from Japanese corpora. In the
study three main approaches were explored:a) analysis of morpheme sequences in Japanese texts to determine the presence of potential new or
unrecorded terms. The processes included processing the texts with a morphological analyzer toproduce sequences of tagged morphemes, tagging of the morphemes with features derived fromcombinatory data derived from large lexicons and corpora, and processing the tagged morphemeswith rule-based and machine-learning-based chunkers to assemble candidate words and expressions.
b) analysis of language patterns which are often used in Japanese in association with new and emerging
terms. These patterns are usually associated with discussions or explanations of new terms. (Breen etal., 2018)c) synthesis of possible Japanese terms by mimicking Japanese morphological processes, followed by
testing for the presence of candidate terms in Japanese corpora.
In this paper we report on the third of these, based on the synthesis of possible Japanese words. (Anothercomponent, covering compound verbs, has been reported separately. (Breen and Baldwin, 2009))
A central issue when dealing with neologisms in Japanese is the nature of the orthography, with its use ofmultiple scripts, primarily kanji (Chinese characters), e.g. 猫, 犬, 鳥, 牛, etc., of which approximately 2,500
are in common use and which are used mainly for nouns and the roots of verbs, adjectives, etc.; and the
hiragana and katakana syllabaries, each of 46 symbols plus diacritics. A major issue is the absence of any
indication of the boundaries between the syntactic elements in texts. Automated text-processing in Japaneseusually relies on morphological analysis software such as MeCab (Kudo, 2008) which employ largemorpheme lexicons such as UniDic (Den et al., 2007), however unrecorded terms will (by definition) usually
not be found in these lexicons.
2. Lexicographical Aspects
The study reported here has concentrated on the identification of neologisms with a view to possibly
including them in a dictionary, and indeed many of those identified have been added to the online JMdict
Japanese dictionary (Breen, 2004)(a major online Japanese dictionary). The process of assessing potential
lexical items for such inclusion is central to lexicography, and along with the establishment of an accuratetranslation into the target language (English in this case) involves a range of processes and issues indetermining their suitability. In our study we have targeted identifying neologisms which are suitable forinclusion in both coding and decoding dictionaries, an important issue as Japanese dictionaries compiled fornative speakers do not typically contain terms such as 凹状 (ōjō– concavity) as it is seen as a prefix-plusnoun (concave shape), whereas the term should, and does, occur in dictionaries intended for non-native
speakers.

The techniques used in this study focussed on terms that occur in corpora in significant enough quantities towarrant further investigation. In addition, several of the techniques identified 2-kanji terms which are
typically nouns, and thus would be strong candidates for lexicalization. There remain questions such aswhether identified compound nouns or multiword expressions have meanings which are idiomatic, novel or
non-intuitive enough to warrant lexicalization. This is widely recognized as one of the major challenges inlexicography (Atkins and Rundell, 2008). Some have expressed the view that with the rise of electronic
dictionaries, which do not have the size limitations of printed dictionaries, there is little harm in relaxing thisevaluation and including larger numbers of non-idiomatic compounds, expressions, etc. In the case ofJapanese there are additional issues such as multiple surface-forms, reading variations, the extensive use ofpseudo-English constructions, etc. to take into account. A detailed analysis of the lexicographic issues
associated with Japanese dictionary entries, including the criteria for lexicalization, is reported
elsewhere (Breen, 2017).
3. Resources
As the experimentation with synthesized terms takes the form of create-and-test, a key requirement is accessto appropriate large-scale corpora to test for the presence and usage patterns of the terms.
Corpora such as the Balanced Corpus of Contemporary Written Japanese (BCCWJ) (Maekawa et al., 2014)
which contain relatively small quantities of specially selected documents, or collections of newspaper
articles (e.g. the Kyoto Corpus), are unlikely to include new or ephemeral terms in quantities that would
enable automated detection and evaluation. The largest collection of accessible text is the WWW, andseveral studies have shown it is broadly representative in areas such as term frequency (Keller and Lapata
2003.) It is appropriate, therefore, to concentrate on WWW-based text collection for analyzing synthesizedterms. (An alternative source of contemporary text is Twitter. A large collection of Twitter-derived text wasused in another part of the neologism detection study.)
The main accessible WWW-derived Japanese corpus for this type of testing is the Google n-gram Corpus(Kudo and Kazawa, 2007), based on approximately 20 billion text segments extracted from WWW pages,
and is provided in the form of sets of 1-grams to 7-grams with counts of the numbers of occurrences. The“grams” in this case are morphemes as identified by morphological analysis software working with largemorpheme lexicons (mentioned above.) As the Google corpus only reports n-grams which occur 20 or moretimes a second n-gram corpus was assembled using the smaller Kyoto University WWW Corpus, containing
about 500 million text segments.
A reference lexicon of known Japanese lexical items is also essential, both for filtering candidate synthesizedcompounds, and for establishing training data for the evaluation processes. A lexicon of over 1.5 million
surface-form/reading combinations was assembled from entries in the following dictionaries: the
Kōjien Japanese-Japanese dictionary (広辞苑) with 229,000 surface-form/reading combinations; theDaijirin Japanese-Japanese dictionary (大辞林) with 215,000 combinations; the JMdict (JapaneseMultilingual dictionary) with 240,000 combinations; the JMnedict (Japanese-Multilingual named-entity
dictionary) with 744,000 combinations; the Kenkyūsha New Japanese-English Dictionary with 128,000
combinations; the NTT Goi Taikei Japanese Lexicon (語彙大系) with 296,000 combinations ; the Japanese
Lexical Database (JLD) from the CJK Dictionary Institute with 302,000 combinations; and miscellaneous
subject-specific glossaries (e.g. legal, biomedical, engineering, etc.) with 329,000 combinations.
Also the most recent UniDic morpheme lexicon (756,000 entries) (Den et al., 2007) is a useful additional
resource as it has a very comprehensive coverage of known bound morphemes in modern Japanese.
4. Investigation Targets
Three types of synthesized term formation were investigated:a) Abbreviation/Clipping. This is a very common and productive process in Japanese, wherein the(usually) leading character of each of the components of a composite are taken to form anabbreviated compound (Tsujimura, 2006, p. 153). An example of this is 学割 gakuwari “student
discount” from the full compound 学生割引 gakuseiwaribiki. The terms produced by this process are
typically nouns or adjectival nouns, and hence if valid would be clear candidates for lexicalization.

a) Affixation. The addition of prefixes and suffixes, often written with a single kanji, is a very common
morphological process in Japanese (Tsujimura, 2006). Vance (1991) describes 63 single-kanji
affixes commonly employed. The process is very productive and the resulting terms are not usuallylexicalized unless they have an idiomatic meaning or unusual reading. Most of the terms arisingfrom this process are nouns, e.g. those arising from the affixation of 化 (ka: -ization). Some others,
such as 的 (teki: -like, -ical, -ish, etc.) form adjectives.
b) Compounding. As in many languages, the formation of terms by combining two or more words ormorphemes is very common. The components can be independent words, as in 秋空 akizora “autumnsky” where both 秋 aki and 空 sora can be used independently, or bound morphemes as in 警告
keikoku “warning” where neither component can be used alone. (See Tanaka (2002) and Baldwin
and Tanaka (2004) for earlier work in this area.) In this study two types of synthesized compounds
were investigated:
i. 2-kanji compounds, as in the examples above;ii. composites formed by aggregating known 2-kanji compounds, for example combining 警告(above) with 射撃 shageki “firing, shooting” forms a composite 警告射撃 keikoku shageki
meaning “warning shot”. This process is very common and the resulting terms often have a clear
sum-of-the-parts meaning, which results in them often not being lexicalized. ( 警告射撃 is inseveral major bilingual dictionaries, but is not in most Japanese dictionaries.)
5. Analysis Approach
At the heart of this task is the development of techniques which will detect if a sequence of characters is
being used as a term in written Japanese. Synthesized terms which were not already recorded in a reference
lexicon were considered for evaluation if they occurred more than 100 times in the corpus (that number
being a reasonable level of “commonness” to warrant recording in a dictionary). The approach which is
being evaluated is based on the assumption that if a compound is actually being used, it will occur in textual
syntactic contexts which are typical for such terms. Japanese uses a large number of particles which areusually written in the hiragana syllabary, and it is the encapsulation by these particles that we use as thebasis of the analysis. For example, if a term is being used as a noun, it could be expected that it is observedin text followed by the が ga subject-marking particle, or the は wa topic-marking particle, and will often
occur in encapsulations such as <が,を> where を wo marks the object of a clause or sentence.
For testing purposes combinations of the following common pre/postpended particles were selected:
pre:
post:

は (wa – topic), が (ga - subject), に (ni – location, indirect object, etc.),の (no – possessive, modifier, etc.), な (na - adjectival), て (te - continuative),
や (ya - conjunction)
を (wo - object), が (ga), に (ni), の (no), な (na), や (ya)The initial tests were carried out using all combinations apart from repeated particles (e.g. が ... が), giving
37 combinations. The n-gram counts for 10 common encapsulations are given in Table 1 for threecompounds: the common noun 男子 (danshi) - youth, young man, which occurs 5 million times in the ngrams, the less common noun: 人魚 (ningyo) - mermaid, merman (607,772 times), and the rather lesscommon 間者 (manja) - spy (24,543 times).
Table 1. Examples of n-gram counts for particle encapsulations
Encapsulation

男子

人魚

間者

はＸを

2631

616

100

はＸが

18242

1002

47

はＸに

9117

1150

26

はＸの

24367

4290

54

はＸな

1135

247

0

はＸや

164

103

0

がＸを

5676

285

33

がＸに

4340

1015

35

がＸの

6363

1703

35

がＸな

423

75

0

As can be seen from the counts associated with these known nouns, there are clear patterns associated withthe encapsulations which have the potential to drive the analysis of synthesized terms to detect actual nouns
among them.
Two types of evaluation were carried out on the sets of counts:a) a machine-learning approach in which models were trained on the features associated with known
terms and non-terms;b) a heuristic approach using rules based on the numbers of encapsulations.
To evaluate whether a machine-learning approach to classifying synthesized compounds can be used in thiscase we tested the application of a support vector machine (SVM) to the problem. SVMs have been
demonstrated to be a very effective approach to this form of classification problem (Joachims 1999;
Steinwart and Christmann 2008). The SVM package used is LIBSVM (Chang and Lin 2011).
To train the SVM a set of 400 terms which met the 100-occurrence threshold was selected and the n-gramcounts of their encapsulations extracted. On examination it emerged that terms with fewer than 5 non-zero
encapsulations were generally not valid nouns, so this was used as the basis for dividing the training data
into valid/non-valid sections. During this initial evaluation it was also determined that there were some
useful refinements possible with the training model:a) seven of the 37 initial encapsulation patterns, e.g. <て,な> and <や,な>, did not contributesignificantly to the evaluation, and hence could be removed from the basic modelb) the addition of the counts with six postpended particles (は, を, が, に, の, な) improved theperformance (this formed an extended model.)The models were tested against a set of 840 synthesized terms which had been hand-checked for theirvalidity. The basic model classified 71 of these, of which 68 were in the reference lexicon and the other 3were valid, albeit rare terms. The extended model classified 83, of which 80 were in the reference lexicon
and the other 3 were also valid but rare terms. This indicated a very high level of precision with thetechnique. The establishment of the level of recall of the technique, i.e. the proportion of valid terms
classified, is a more of a problem as a valid but rarely-occurring term is unlikely to be classified by themodels. Further testing with randomly-selected sets of valid terms indicated an overall recall of around 70%.
6. Analysis of Synthesized Abbreviations
As mentioned earlier, abbreviations in Japanese are typically formed by taking the leading character of themorpheme components of the full term, as in the case of 学割/学生割引. There are known exceptions, forexample the common term for “high school” is 高校 kōkō, which is formed from the first character of 高等kōtō (high grade) and the second character of 学校 gakkō (school). A brief test of the validity of this(common) assumption was carried out by examining the sources of a sample of 75 known abbreviations.
78% followed the leading morpheme character pattern, with the remainder either coming from non-initialcharacters (as with 高校) or from multi-morpheme source terms where some morphemes did not contributeto the abbreviation. It was considered that the 78% validated the approach sufficiently to warrant continuing
the approach.
Using the leading-character method, 33,000 possible abbreviations were generated from known multimorpheme terms. Of these, 7,900 were not already in the reference lexicon and met the threshold of 100
occurrences in the n-gram corpus. The SVM classifier identified 162 for further consideration. Inspection ofthese determined:

15 were abbreviations of the source term;

33 were valid terms, but not identifiable as abbreviations of the source term, For example 一話 had
been generated from 一夕話 issekiwa (short story) but is actually a different term meaning “episode(of a series, serial, etc.)”;the remainder were mostly on-the-fly compound collocations such as 五年 go'nen “five years”, 三万sanman “thirty thousand”, 主論 shuron “principal theory”, etc. Constructions using productive
affixes such as 的 teki “-like” and 化 ka “-ification” were quite common too.

It must be concluded that to find 15 abbreviations after processing 33,000 source compounds certainly
indicates it is not a highly productive technique. With the level of precision resulting from this test being
only 9%, one must conclude there are probably better ways of searching for new unrecorded terms.
7. Analysis of Affixation
As mentioned above, affixation, often through the use of single-kanji prefixes and suffixes, is a very
common and productive morphological process in Japanese. For example the noun 衒学 gengaku “pedantry”
can take suffixes such as the personalizing suffix 者 sha to form 衒学者 gengakusha “pedant” or theadjective-forming suffix 的 to form 衒学的 gengakuteki “pedantic”.
There is the potential to synthesize extended compounds using these affixes and test for their presence inJapanese texts. However initial testing demonstrated that the productiveness of this morphological process is
so high, and in general the meanings of the extended compounds are so predictable, that as expected theresulting terms are usually lightly lexicalized. The reasons for considering the identification and possible
lexicalization of such terms include:a) idiomatic meanings which are not readily apparent from the components;b) covering targets for English-Japanese dictionary entries;c) special meanings in certain contexts, e.g. 拮抗 kikkō “rivalry, antagonism” where 拮抗的 kikkōteki
is used in medical contexts for “antagonistic” and can be found in formations such as 拮抗薬
kikkōyaku “antagonist drug”.
The challenge is to determine whether proceeding further with this particular investigation could bring up
anything useful. Certainly a straight extraction process is virtually guaranteed to identify large numbers ofunlexicalized formations, however identifying those worthy of lexicalization, e.g. those with idiomatic or
special meanings, is largely outside the scope of this study.
8. Analysis of Compound Generation
The development of the techniques described earlier allows for the rapid testing of large numbers ofsynthesized compounds against the n-gram Corpus, and are achieving high levels of precision and generally
satisfactory levels of recall. While there has been some success with targeted compound synthesis in theform of potential abbreviation formation, there is also the possibility of simply generating kanji combinations
using commonly occurring kanji, and using the classification system as a filter to detect combinations which
are actually being used as terms.
While the full number of kanji in use in written Japanese is very large, only about 2,500 are in common use,
leading to over 6 million possible 2-kanji compounds. While this is a relatively large number, it is a quitemanageable processing task to: create synthetic compounds simply by combining kanji;
 filter out known compounds;
 classify the remaining compounds using the techniques described above.
Initial tests were carried out using 10,000 compounds generated from combinations of the 100 most common
kanji. Of these approximately 2,700 were in the in the reference lexicon, and of the remainder 97 wereclassified using the SVM model described earlier. Inspection of the 97 classified compounds revealed thatmany involved numerics (二円 ni’nen “two yen, circles, etc.”, 十五 jūgo “fifteen”, etc.) and several involved
very common compositional suffixes such as 氏 shi “surname”, e.g. 東氏 higashishi “Mr Higashi”. This
resulted in a form of noise that tended to obscure some valid compounds in the set, e.g. 化調 kachō which isan unrecorded abbreviation of 化学調味料 kagakuchōmiryō “chemical seasoning”. Sufficient valid results
were detected that it was decided to investigate more thoroughly using less common kanji and excluding the
major single-kanji affixes, which it was hoped would not produce as many obvious compositional
compounds.
8.1. 2-kanji Compound Generation and Testing
In carrying out large-scale testing of synthesized 2-kanji compounds, the SVM classification systemdescribed earlier was supplemented in several ways:a) three additional sets of training data were created of 10,000, 40,000 and 90,000 compounds
respectively (small, medium and large models). Compounds which did not appear in the n-gram
corpora or did not have a minimum number of encapsulation features were removed, and theremainder were checked against the reference lexicon to establish a valid/not-valid classification.
b) both the Google and Kyoto models were used in parallel to test whether the 20 n-gram cut-off in the
Google corpus had a significant impact.
c) a heuristic approach based on the number of encapsulation features was also tested.
Two testing sets each of 40,000 compounds were synthesized, and then filtered against the criteria of having
n-gram counts and meeting a minimum number of counted encapsulations. For example, in the first set 202
compounds were counted in the Google corpus and had counts for 5 or more features. Of these 202, 135
were in the reference lexicon. As expected somewhat larger numbers were counted in the Kyoto corpus as
the cut-off is lower.
The filtered compounds were then classified by the various SVM models. For example of the 202
compounds mentioned above 89 of the 135 in-lexicon compounds were classified along with 20 of the 67
not-in-lexicon compounds. The overall assessment of these classification tests was:a) the overall precision, using presence in the lexicon as the criterion, was around 75% across themodels;b) the recall level, i.e. the proportion of in-lexicon terms identified, varied across the models and washighest with the Google/5+ features model at around 65%.
c) virtually identical results were achieved by using the feature count heuristic.
It is inappropriate, however, to focus too much on the effectiveness of the models in recovering known terms
as it loses sight of the actual goal, which is to identify terms which are not in references. To evaluate thatproperly, we needed to look in detail at the compounds which have been classified but did not occur in thereference lexicon. A total of 38 unlexicalized compounds from the first test set which had either beenclassified by the Google model or had 7 or more features were examined in detail, using reference material
and the WWW pages in which they were used. The compounds turned out to consist of 11 nouns, 23 namedentities and 4 non-terms (apparently resulting from mis-parses in the n-gram generation). The kanji ranges
used in the generation happened to include kanji such as 霊 (spirit), 魔 (demon), 姫 (princess), 鬼 (ogre), 龍(dragon), 雷 (lightning), etc. thus virtually guaranteeing the generation of a number of manga, anime, game,
etc. character names. Since the investigation is not attempting to make value judgements about thecompounds, but needs to assess them as to whether they are valid terms in use which may be incorporated insome lexicon (even one of manga characters), it has to be concluded that 34 of the 38 are valid, and hence
the overall precision achieved was 89.5%. The second set of 40,000 compounds was also evaluated, with asimilar outcome. It was observed that valid terms identified by the process tended to have larger numbers ofencapsulation features, indicating that a heuristic approach based on this is probably more effective thatusing machine learning.
While the overall yield of valid unlexicalized compounds detected during the testing is, predictably, notparticularly high, the level of precision, approaching 90%, indicates that this technique has considerable
potential for being used on a large scale to identify potential neologisms which can be passed onto detailed
analysis.
Some examples of valid unlexicalized 2-kanji compounds detected during the investigation are in Table 2.

Table 2. Examples of synthesized 2-kanji compounds

CompoundMeaning

桃豆 momomame

bean-based sweet rolled in peach powder

低弦 teigenlow-pitched string instruments (abbr)

周堤 shūtei

circular bank, e.g. round Jōmon-period grave sites

整膚 seifu

alternative therapy involving pinching and pulling the skin

移弦 igenstring-crossing (violin, etc. technique)

紙筒 kamidzutsu

paper tube (var. of 紙管 shikan)

製縫 seihōsuffix to business names meaning clothes-making

賞暦 shōreki

awards, list of years prizes were won

丸刀 gantō/marutō

gouge with U-shaped blade

士紳 shishinarchaic term for ranking official

帰寮 kiryō

returning to one’s accommodation, etc.

春苗 shunbyō

spring seedlings (also a girl’s name)母珠 moshularge bead(s) in a Buddhist rosary

白粒 shirochibu

type of ceramic used in Kitaniware, etc.

紙函 kamibako

paper box

親鴨 oyagamo

can mean “parent duck”, but mostly used in 親鴨会, a society of former IBM staff

8.2. 4-kanji Compound Generation and Testing
The potential numbers of 4-kanji compounds which can be generated from known 2-kanji compounds is verylarge, for example if we take the approximately 50,000 2-kanji compounds in the JMdict data as a basis forgeneration we will get several billion candidates. The total number of recorded 4-kanji compounds is,however, in the 100-200,000 range so it can be expected that the proportion of generated compounds which
turn out to be valid will be very small.
For the testing of synthesized 4-kanji compounds a similar approach to the 2-kanji compounds has been
followed. In this case the generation process was combined with the initial filtering, and compounds were
only passed to the classification stage if they reached a threshold count in the Google n-grams (initially
1,000, later reduced to 100), and recorded a minimum number of 5 or more encapsulation features. In an
initial test of 1,000,000 compounds, 310 met the 1,000 count threshold and of these 181 had had the required
number of features. 80 of these were classified by the SVM model, of which 16 were in the reference
lexicon. A selection of the classified compounds is in Table 3. Reducing the count threshold to 100
increased the candidate compounds by 29, however no more were classified and only two more of theunclassified compounds were in the lexicon (also in the table.)
Table 3. Examples of classified 4-kanji compounds
CompoundCount

Features

LexiconMeaning

一軒一軒 ikken’ikken

48601

22

Y

house-to-house

一言一句 ichigon’ikku

29723

15

Y

word-by-word

一向一揆 ikkōikki

30691

27

Y

Jodo Shinshu Buddhist uprising

一個一個 ikkoikko

72951

24

N

one-by-one

一曲一曲 ikkyokuikkyoku

49027

23

N

piece-by-piece (in music)

一言一行 ichigen’ikkō

533

9

Y

every word and act一齣一齣 hitokomahitokoma

258

8

Y

frame by frame

The fact that the 2-kanji compounds used for generation contained a set beginning with the kanji 一 ichi,
hitotsu “one” was somewhat unfortunate; all but 7 of the classified compounds began with 一. It appears
there are many non-idiomatic expressions of the 一 X 一 X and 一 X 一 Y variety with meanings such as “X
occurring one after another” or “one X per Y” which are not necessarily lexicalized. As this distorted theresults estimation of the precision in this sample was not attempted
A further two sets of 1,000,000 compounds were tested of which 115 met the criteria for classification. The
numbers classified and whether they were in the lexicon is in Table 4.
Table 4. Evaluation outcome for synthesized 4-kanji compounds
In Lexicon

Not In Lexicon

Classified

917

Not Classified

8

81

Examination of the classified out-of-lexicon compounds determined that several are real terms:
英国王室 eikokuōshitsu - “British royal family”
液晶演出 eishōenshutsu - name of a video/pachinko game
欧州遠征 ōshūensei - “European campaign” (esp. with sporting teams)
横浜駅前 yokohamaekimae - a typical address, in this case at the front of Yokohama Station
下限価格 kagenkakaku - “price floor”
化粧下地 keshōshitaji - “foundation base”
Most of the in-lexicon but unclassified compounds were rather obscure, e.g. 寡占価格 kasenkakaku“oligopolistic price” has a low count and barely made the list to be tested.
One observation from this investigation of 4-kanji compounds is that identifying candidates on the basis ofn-gram counts and feature numbers almost always out-performed the SVM machine-learning approach.
Moreover, it usually identified a compound which was actually being used in text. This is not that surprisingwhen one considers the process of forming these sorts of extended compounds in Japanese - one has
considerable freedom and flexibility. It is not unreasonable to conclude that almost of the 4-kanji compounds
passing the (heuristic-driven) filters are actual compounds being used. Quite possibly this part of the study
needs to be viewed from the position of discovering 4-kanji compounds that are in common enough use to beconsidered for lexicalization. That consideration needs to take into account issues such as:a) whether the compound has an idiomatic meaning (e.g. 援助交際 enjokōsai, where the component
compounds mean “assistance” and “company”, but the full compound is a common euphemism forteenage prostitution.)b) whether it is a useful inclusion for reasons such as for English-to-Japanese reverse searches.
A point to note is that many of the accepted terms have meanings which are readily apparent from thecomponents, and hence are unlikely to be included in a general dictionary unless the usual criteria were
relaxed. It was noted that compounds formed from components which were polysemous were more likely tohave non-intuitive meanings, which indicates a possibly fruitful area of future study.

9. Conclusion
In this paper we describe the investigation of a neologism detection approach involving the synthesis ofpossible Japanese terms by mimicking Japanese morphological processes, followed by testing for thepresence of candidate terms in Japanese corpora in appropriate syntactic contexts. Synthesized terms which
passed this evaluation were assessed by regular lexicographic processes to determine their suitability for
lexicalization.
Of the techniques tested: abbreviation, affixation and compounding, the latter showed particular promise,with the 2-kanji compound generation and classification resulting in significant numbers of unrecorded terms
deemed suitable for inclusion in dictionaries.
The investigation used both a SVM-based machine-learning technique and heuristics to identify neologisms,
and concluded that the simpler heuristic approach performed marginally better. It would be interesting as
future work to test the use of a neural network approach, as these have proved very successful in languageprocessing applications. Given that the techniques used in this investigation achieved relatively high levels of
precision, any improvement would probably be small.
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