Extraction of named entities from tables in gene mutation literature

We investigate the challenge of extracting information about genetic mutations from tables, an important source of information in scientific papers. We use various machine learning algorithms and feature sets, and evaluate performance in extracting fields associated with an existing handcreated database of mutations. We then show how classifying tabular information can be leveraged for the task of named entity detection for mutations.