Biomedical named entity recognition (BNER) has been actively studied over the years, and several BNER systems have become publicly available. In this study, we investigate the utility of a simple voting method called at-least-n voting to improve gene name recognition, which takes advantage of the availability of BNER systems in the domain. We found this voting scheme is effective in combining B...

We present two methods for learning the structure of personal names from unlabeled data. The first simply uses a few implicit constraints governing this structure to gain a toehold on the problem — e.g., descriptors come before first names, which come before middle names, etc. The second model also uses possible coreference information. We found that coreference constraints on names improve the...

We present a named entity recognition (NER) system for tagging fiction: LitNER. Relative to more traditional approaches, LitNER has two important properties: (1) it makes no use of handtagged data or gazetteers, instead it bootstraps a model from term clusters; and (2) it leverages multiple instances of the same name in a text. Our experiments show it to substantially outperform off-the-shelf s...