The Company They Keep: Extracting J apanese Neologisms Using Language Patterns (2018)

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

## 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, and existing 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 the language 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 the term 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 combined with the reading in parentheses, and then followed by an explanation. the phenomenon is well known to japanese translators, who often 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 so targeted by those language patterns. ## prior work research into the use of linguistic patterns in text to detect terms of interest has taken place in several contexts. in keyphrase extraction hasan and ng (2014) have produced a wide-ranging survey of the various techniques used in keyphrase extraction and their relative effectiveness, and @xcite evaluate the performance of a variety of supervised and unsupervised approaches. in term extraction, which is a major part of the broader field of terminology, usually in technical contexts @xcite ), @xcite adapted the french acabit system, which detects morpho-syntactic sequences, to isolate terms in japanese for later analysis. @xcite used patterns of phrases to identify particular japanese legal documents of interest. @xcite 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 their survey of collocations in language processing, @xcite explore the role of the extraction of collocations in lexicography, although the focus is on the identification of general terms rather than those which are highlighted 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. @xcite 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). ## text corpora an essential element of the investigation is the availability of substantial quantities of japanese text, preferably from a variety of sources. while there are number of japanese corpora available for use in nlp work, most are actually quite small. in this study we used two text collections: a. the kyoto www corpus. this is a collection of 500 million japanese sentences collected 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 be recorded 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. ## 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 of group 2/3 being detected in the sampling. excluded from the original set were three of 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 the sentence 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 from two 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. ## 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) in which 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: also recorded was whether the occurrences of the terms were parenthesized or not, and which pattern(s) generated the extraction. (this was done for the "c" terms.) ## 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 prepared to 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 the text, 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. ## discussion and conclusions from the investigations described above, a number 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 for further investigation, as it identifies candidates that are often very worthy of detailed examination and subsequent lexicalization. b. it is interesting and not a little frustrating that 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 of useful terms is so low as to make them of little 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 by association 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 the recall 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, and passing extracted candidate terms through a frequency and n-gram analysis, and ultimately on to lexicographers for analysis.