Nobata C, Tsujii J i: Extracting the names of genes and gene products with a hidden Markov model

We report the results of a study into the use of a linear interpolating hidden Markov model (HMM) for the task of extracting technical ter-minology from MEDLINE abstracts and texts in the molecular-biology domain. This is the rst stage in a system that will extract event information for automatically updating biology databases. We trained the HMM entirely with bigrams based on lexical and character fea-tures in a relatively small corpus of 100 MED-LINE abstracts that were marked-up by do-main experts with term classes such as proteins and DNA. Using cross-validation methods we achieved an F-score of 0.73 and we examine the contribution made by each part of the interpo-lation model to overcoming data sparseness.