Comparison of named entity recognition methodologies in biomedical documents

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

Named Entity Recognition in Vietnamese documents

Named Entity Recognition (NER) aims to classify words in a document into pre-defined target entity classes and is now considered to be fundamental for many natural language processing tasks such as information retrieval, machine translation, information extraction and question answering. This paper presents the results of an experiment in which a Support Vector Machine (SVM) based NER model is ...

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...