Discriminative Application of String Similarity Methods to Chemical and Non-chemical Names for Biomedical Abbreviation Clustering

Term clustering by measuring the string similarities between terms is known to be an effective method to improve the quality of texts and dictionaries. However, based on our observations, chemical names are difficult to cluster using string similarity measures such as the edit distance. To demonstrate this difficulty clearly, we compared the string similarities determined using the edit distance, the Monge-Elkan score, SoftTFIDF, and the bigram Dice coefficient for chemical names with those for other terms. The experimental results show that the discriminative application of string similarity methods to chemical and non-chemical names may be a simple but effective way to improve the performance of term clustering.