Distilling Knowledge with a Teacher’s Multitask Model for Biomedical Named Entity Recognition

Biomedical Named Entity Recognition: A Poor Knowledge HMM-Based Approach

With a recent quick development of a molecular biology domain it becomes indispensable to promote different resources as databases and ontologies that represent the formal knowledge of the domain. As these resources have to be permanently updated, due to a constant appearance of new data, the Information Extraction (IE) methods become very useful. Named Entity Recognition (NER), that is conside...

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

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...

Named Entity Recognition in Biomedical Texts using an HMM Model

Although there exists a huge number of biomedical texts online, there is a lack of tools good enough to help people get information or knowledge from them. Named Entity Recognition (NER) becomes very important for further processing like information retrieval, information extraction and knowledge discovery. We introduce a Hidden Markov Model (HMM) for NER, with a word similarity-based smoothing...