Text normalization is an essential task in the processing and analysis of social media that is dominated with informal writing. It aims to map informal words to their intended standard forms. Previously proposed text normalization approaches typically require manual selection of parameters for improved performance. In this paper, we present an automatic optimizationbased nearest neighbor matching approach for text normalization. This approach is motivated by the observation that text normalization is essentially a matching problem and nearest neighbor matching with an adaptive similarity function is the most direct procedure for it. Our similarity function incorporates weighted contributions of contextual, string, and phonetic similarity, and the nearest neighbor matching involves a minimum similarity threshold. These four parameters are tuned efficiently using grid search. We evaluate the performance of our approach on two benchmark datasets. The results demonstrate that parameter tuning on small sized labeled datasets produce state-of-the-art text normalization performances. Thus, this approach allows practically easy construction of evolving domain-specific normalization lexicons
Typical works on text normalization have relied primarily upon string and phonetic similarity in an hierarchical candidate generation and filtering procedure for identifying lexical variations of IV words (). For example, @cite_0 present a technique that generates a confusion set' for IV words by filtering out OOV words using edit distance and phonetic measures, followed by ranking based on a tri-gram language model.
@cite_0
2131151781
We present an approach for the construction of text similarity functions using a parameterized resemblance coefficient in combination with a softened cardinality function called soft cardinality. Our approach provides a consistent and recursive model, varying levels of granularity from sentences to characters. Therefore, our model was used to compare sentences divided into words, and in turn, words divided into q-grams of characters. Experimentally, we observed that a performance correlation function in a space defined by all parameters was relatively smooth and had a single maximum achievable by "hill climbing." Our approach used only surface text information, a stop-word remover, and a stemmer to tackle the semantic text similarity task 6 at SEMEVAL 2012. The proposed method ranked 3rd (average), 5th (normalized correlation), and 15th (aggregated correlation) among 89 systems submitted by 31 teams.