Social media language contains huge amount and wide variety of nonstandard tokens, created both intentionally and unintentionally by the users. It is of crucial importance to normalize the noisy nonstandard tokens before applying other NLP techniques. A major challenge facing this task is the system coverage, i.e., for any user-created nonstandard term, the system should be able to restore the correct word within its top n output candidates. In this paper, we propose a cognitivelydriven normalization system that integrates different human perspectives in normalizing the nonstandard tokens, including the enhanced letter transformation, visual priming, and string/phonetic similarity. The system was evaluated on both word-and messagelevel using four SMS and Twitter data sets. Results show that our system achieves over 90% word-coverage across all data sets (a 10% absolute increase compared to state-ofthe-art); the broad word-coverage can also successfully translate into message-level performance gain, yielding 6% absolute increase compared to the best prior approach.
The study REF designs a normalization system that integrates enhanced letter transformation, visual priming, and string/phonetic similarity for SMS and Twitter data sets.
A Broad-Coverage Normalization System for Social Media Language
ACL
Computer Science