  Recent work on segmentation-free word embedding(sembei) developed a new
pipeline of word embedding for unsegmentated language while avoiding
segmentation as a preprocessing step. However, too many noisy n-grams existing
in the embedding vocabulary that do not have strong association strength
between characters would limit the quality of learned word embedding. To deal
with this problem, a new version of segmentation-free word embedding model is
proposed by collecting n-grams vocabulary via a novel unsupervised association
measure called pointwise association with times information(PATI). Comparing
with the commonly used n-gram filtering method like frequency used in sembei
and pointwise mutual information(PMI), the proposed method leverages more
latent information from the corpus and thus is able to collect more valid
n-grams that have stronger cohesion as embedding targets in unsegmented
language data, such as Chinese texts. Further experiments on Chinese SNS data
show that the proposed model improves performance of word embedding in
downstream tasks.
