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Recent work on segmentation-free word embedding(sembei) developed a newpipeline of word embedding for unsegmentated language while avoidingsegmentation as a preprocessing step. However, too many noisy n-grams existingin the embedding vocabulary that do not have strong association strengthbetween characters would limit the quality of learned word embedding. To dealwith this problem, a new version of segmentation-free word embedding model isproposed by collecting n-grams vocabulary via a novel unsupervised associationmeasure called pointwise association with times information(PATI). Comparingwith the commonly used n-gram filtering method like frequency used in sembeiand pointwise mutual information(PMI), the proposed method leverages morelatent information from the corpus and thus is able to collect more validn-grams that have stronger cohesion as embedding targets in unsegmentedlanguage data, such as Chinese texts. Further experiments on Chinese SNS datashow that the proposed model improves performance of word embedding indownstream tasks.

Improving Chinese Segmentation-free Word Embedding With Unsupervised Association Measure