Performance of Gene Name Recognition Tools on Patents

The accurate identification of gene and protein names in patents is an essential step in many commercially highly relevant applications, such as patent retrieval, prior art search, or patent classification. Since patents exhibit a number of properties that make them quite different from scientific articles, it is questionable whether tools developed for the latter sort of texts will work equally well for the former. Answering this question is aggravated by the fact that only few annotated patent corpora exist which makes training hard. In this paper, we report on a comparative evaluation of four existing gene/protein named entity recognition and normalization tools trained on scientific articles regarding their performance on the two patent corpora. We analyze the tools with respect to different evaluation metrics to highlight their respective strengths and limitations. Our results reveal that the performances of these tools over patents are generally lower than for scientific articles. Exemplified by one of the four tools, we also show that training on annotated patents considerably improves performance on patent corpora. We conclude that more efforts must be taken to produce adequate training data for working with patents. keywords: Patent Mining, Named Entity Recognition, Named Entity Normalization, Gene and Protein Entities, Performance Measurements.