Biomedical Named Entity Recognition at Scale

Biomedical Named Entity Recognition System

We propose a machine learning approach, using a Maximum Entropy (ME) model to construct a Named Entity Recognition (NER) classifier to retrieve biomedical names from texts. In experiments, we utilize a blend of various linguistic features incorporated into the ME model to assign class labels and location within an entity sequence, and a postprocessing strategy for corrections to sequences of ta...

Unsupervised Biomedical Named Entity Recognition

At-least-N voting over biomedical named entity recognition systems

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...

Biomedical Named Entity Recognition Using Neural Networks

We investigate the task of Named Entity Recognition (NER) in the domain of biomedical text. There is little published work employing modern neural network techniques in this domain, probably due to the small sizes of human-labeled data sets, as non-trivial neural models would have great difficulty avoiding overfitting. In this work we follow a semi-supervised learning approach: We first train s...

Reranking for Biomedical Named-Entity Recognition

This paper investigates improvement of automatic biomedical named-entity recognition by applying a reranking method to the COLING 2004 JNLPBA shared task of bioentity recognition. Our system has a common reranking architecture that consists of a pipeline of two statistical classifiers which are based on log-linear models. The architecture enables the reranker to take advantage of features which...