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The Chinese language poses challenges for natural language processing basedon the unit of a word even for formal uses of the Chinese language, socialmedia only makes word segmentation in Chinese even more difficult. In thisdocument we propose a pointillism approach to natural language processing.Rather than words that have individual meanings, the basic unit of apointillism approach is trigrams of characters. These grams take on meaning inaggregate when they appear together in a way that is correlated over time.  Our results from three kinds of experiments show that when words and topicsdo have a meme-like trend, they can be reconstructed from only trigrams. Forexample, for 4-character idioms that appear at least 99 times in one day in ourdata, the unconstrained precision (that is, precision that allows for deviationfrom a lexicon when the result is just as correct as the lexicon version of theword or phrase) is 0.93. For longer words and phrases collected fromWiktionary, including neologisms, the unconstrained precision is 0.87. Weconsider these results to be very promising, because they suggest that it isfeasible for a machine to reconstruct complex idioms, phrases, and neologismswith good precision without any notion of words. Thus the colorful and baroqueuses of language that typify social media in challenging languages such asChinese may in fact be accessible to machines.

A Pointillism Approach for Natural Language Processing of Social Media