[
Table 11: Performance of other clustering methods and variations in our framework on SMS (small) dataset.
['[EMPTY]', 'Experiment', 'Precision', 'Recall', 'F-measure']
Experiment 1 is a rule-based lexical normalization method. Experiments 2 and 3 are independent clustering methods for lexical normalization. We also modify string features (experiments 4 and 5), phonetic features (experiment 6), and contextual features (experiments 7, 8, 9, and 10), respectively, in our clustering framework. Han et al. IV (in-vocabulary) pairs using contextual similarity and then re-ranking them based on string and phonetic distances. In contrast, we define a similarity function that considers all three features together to find lexical variations of a word. Unlike previous approaches, we have a small corpus from which to extract contextually similar word pairs. Also, there is no standard Roman Urdu dictionary that can be used to annotate words as either IV or OOV. We use the longest common subsequence as part of the string similarity feature. Li and Liu Singh et al. Rangarajan Sridhar et al.