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 to produce sequences of tagged morphemes, tagging of the morphemes with features derived from combinatory data derived from large lexicons and corpora, and processing the tagged morphemes with 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 et al., 2018) c. synthesis of possible Japanese words by mimicking Japanese morphological processes, followed by testing for the presence of candidate words in Japanese corpora. In this paper we report on the third of these, based on the synthesis of possible Japanese words. (Another component, 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 of multiple 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 Japanese usually relies on morphological analysis software such as MeCab (Kudo, 2008) which employ large morpheme lexicons such as UniDic (Den et al., 2007), however unrecorded terms will (by definition) usually not be found in these lexicons.
2
Resources
As the experimentation with synthesized terms takes the form of create-and-test, a key requirement is access to appropriate large-scale corpora to test for the presence and usage patterns of the terms. The main accessible 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. As the Google corpus only reports n -grams which occur 20 or more times a second n -gram corpus was assembled using the smaller Kyoto University WWW Corpus, containing about 500 million text segments. Investigation Approach 3 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 an abbreviated compound. An example of this is学割gakuwari "student discount" from the full compound学生勤引gakuseiwaribiki. b. 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 usually lexicalized unless they have an idiomatic meaning or unusual reading. c. Compounding.  As in many languages, the formation of terms by combining two or more words or morphemes is very common. The components can be independent words, as in秋空akizora “autumn sky” 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.)  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”. Synthesized terms which were not already recorded in a reference lexicon were considered if they occurred more tham 100 times in the corpus. The evaluation of such terms was carried out by examining their occurrences in a combination of syntactic contexts. Japanese uses a large number of particles which are typically written in the hiragana syllabary.  Counts of occurrences were extracted for the terms encapsulated in 37 combinations of the following common pre/postpended particles. pre:は(wa),が(ga),に(ni),の(no),な(na),て(te),や(ya) post:を(wo),が(ga),に(ni),の(no),な(na),や(ya) Two types of evaluation were carried out on the sets of counts: a. a machine-learning analysis using support-vector machine (SVM) models trained the patterns of counts from a range of established terms (Chang and Lin, 2011); b. a heuristic approach using rules based on the numbers of encapsulations.
4   Investigation Outcome
a.  Abbreviation/Clipping.  Of 33,000 synthesized abbreviations 7,900 were evaluated of which 162 were identified as potential new nouns (2.0%). Hand-checking a sample revealed that few were actually valid abbreviations. Most were other types of collocations. b. Affixation. Initial testing of potential terms generated by this technique resulted in large numbers which were clearly in regular use. It quickly emerged, however, that they almost always had quite predictable meanings and were unlikely to be included in a dictionary. For example the noun街学gengaku "pedantry" can take suffixes such as the personalizing suffix者sha to form街学者gengakusha "pedant" or the adjective-forming suffix的teki to form街学的gengakuteki “pedantic”.
c. Compounding.
i. 2-kanji compounds. As there are over 6 million combinations of the most common 2,500 kanji , two samples each of 40,000 compounds were generated from two ranges of kanji from which were excluded kanji for numerics, common affixes, etc.  The unlexicalized compounds were tested against the two corpora, resulting in 200-500 compounds being classified from each sample.  Hand-checking selections of these revealed that most were valid terms, with about 60% being proper names. Examples of such terms include移弦igen “string-crossing (violin, etc. technique)”,春苗shunbyō “spring seedlings” (also a girl's name), and母珠moshu "large bead(s) in a Buddhist rosary". The precision of the technique, i.e. the proportion of classified terms which proved to be valid, was quite high. ii.  4-kanji compounds.  The potential numbers of 4-kanji compounds which can be generated from known 2-kanji compounds is very large, however few record sufficient counts in the n-gram corpora to be considered further. Several batches of 1 million compounds were generated, with 400-500 from each being accepted for further analysis, and 30-50 being flagged by the models as potential terms. Again hand-checking confirmed their validity, with terms such as英国王室eikokuōshitsu "British royal family", and欧州遠征ōshūensei "European campaign (esp. with sporting teams)" being identified. A point to note is that many of the accepted terms have meanings which are readily apparent from the components, and are unlikley to be included in a general dictionary. As mentioned earlier, the synthesized compounds were tested using both machine-learning and heuristic models. It was noted that in general the heuristic models performed more effectively than the machine-learning approach.
5
Conclusion
In this paper we describe 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 of candidate words in Japanese corpora.  Of the techniques tested: abbreviation, affixation and compounding, the latter showed particular promise, with the 2-kanji compound generation and classification resulting significant numbers of unrecorded terms.