The informal nature of social media text renders it very difficult to be automatically processed by natural language processing tools. Text normalization, which corresponds to restoring the non-standard words to their canonical forms, provides a solution to this challenge. We introduce an unsupervised text normalization approach that utilizes not only lexical, but also contextual and grammatical features of social text. The contextual and grammatical features are extracted from a word association graph built by using a large unlabeled social media text corpus. The graph encodes the relative positions of the words with respect to each other, as well as their part-ofspeech tags. The lexical features are obtained by using the longest common subsequence ratio and edit distance measures to encode the surface similarity among words, and the double metaphone algorithm to represent the phonetic similarity. Unlike most of the recent approaches that are based on generating normalization dictionaries, the proposed approach performs normalization by considering the context of the non-standard words in the input text. Our results show that it achieves state-ofthe-art F-score performance on standard datasets. In addition, the system can be tuned to achieve very high precision without sacrificing much from recall.
REF present a word association graph that encodes the position of each word with respect to other words.
A Graph-based Approach for Contextual Text Normalization
EMNLP
Computer Science