In this paper, we propose a syllable-based method for tweet normalization to study the cognitive process of non-standard word creation in social media. Assuming that syllable plays a fundamental role in forming the non-standard tweet words, we choose syllable as the basic unit and extend the conventional noisy channel model by incorporating the syllables to represent the word-to-word transitions at both word and syllable levels. The syllables are used in our method not only to suggest more candidates, but also to measure similarity between words. Novelty of this work is three-fold: First, to the best of our knowledge, this is an early attempt to explore syllables in tweet normalization. Second, our proposed normalization method relies on unlabeled samples, making it much easier to adapt our method to handle non-standard words in any period of history. And third, we conduct a series of experiments and prove that the proposed method is advantageous over the state-of-art solutions for tweet normalization.
Whereas most previous work normalizes on the word level or character level, REF attempt to normalize on the syllable level.
Tweet Normalization with Syllables
ACL
Computer Science