Improved Chemical Text Mining of Patents with Infinite Dictionaries and Automatic Spelling Correction

The text mining of patents of pharmaceutical interest
poses a number
of unique challenges not encountered in other fields of text mining.
Unlike fields, such as bioinformatics, where the number of terms of
interest is enumerable and essentially static, systematic chemical
nomenclature can describe an infinite number of molecules. Hence,
the dictionary- and ontology-based techniques that are commonly used
for gene names, diseases, species, etc., have limited utility when
searching for novel therapeutic compounds in patents. Additionally,
the length and the composition of IUPAC-like names make them more
susceptible to typographic problems: OCR failures, human spelling
errors, and hyphenation and line breaking issues. This work describes
a novel technique, called CaffeineFix, designed to efficiently identify
chemical names in free text, even in the presence of typographical
errors. Corrected chemical names are generated as input for name-to-structure
software. This forms a preprocessing pass, independent of the name-to-structure
software used, and is shown to greatly improve the results of chemical
text mining in our study.