Leveraging network analysis to evaluate biomedical named entity recognition tools

Leveraging Existing Tools for Named Entity Recognition in Microposts

With the increasing popularity of microblogging services, new research challenges arise in the area of text processing. In this paper, we hypothesize that already existing services for Named Entity Recognition (NER), or a combination thereof, perform well on microposts, despite the fact that these NER services have been developed for processing long-form text documents that are well-structured ...

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...

Biomedical Named Entity Recognition: A Survey of Machine-Learning Tools

It is well known that the rapid growth and dissemination of the Internet has resulted in huge amounts of information generated and shared, available in the form of textual data, images, videos or sounds. This overwhelming surge of data is also true for specific areas such as biomedicine, where the number of published documents, such as articles, books and technical reports, is increasing expone...

Unsupervised Biomedical Named Entity Recognition

Biomedical Named Entity Recognition based on Deep Neutral Network

Many machine learning methods have been applied on the biomedical named entity recognition and achieve good results on GENIA corpus. However most of those methods reply on the feature engineering which is labor-intensive. In this paper,huge potential feature information represented as word vectors are generated by neutral networks based on unlabeled biomedical text files. We propose a Biomedica...