This study presents a semi-automatic approach to identifying Chinese new words that occurred between 2012 and 2014 in the China Times and United Daily News corpora in Taiwan based on the quantitative evidence of the frequency distributions of ngrams. We first extracted bigrams, trigrams, and 4 grams from the news corpora and filtered out ngrams which contained characters which were not Chinese. We recorded the frequency of each ngram in each year between 2006 and 2014 and identified ngrams whose annual frequency between 2012 and 2014 exceeded their annual frequency between 2006 and 2011. These ngrams were candidates for new words. The frequency of a candidate ngram in a given year was then modelled by the linear equation Y = aX + b, where X was the year and Y was the frequency of an ngram in that year. The Principle of least squares was used to derive the linear equation. We used two linear models to predict the beginning year when the new word first occurred between 2012 and 2014. The year with the smallest average error was considered the beginning year of the new word. Each candidate ngram that was likely to be new word was manually checked against its frequency distributions and its concordances in the news corpora. After human inspection of the candidate words, we identified 12 two-character new words, 26 three-character new words, and 61 four-character new words which occurred between 2012 and 2014.
