Published Paper Details:

With the rapid advancement of technology and the necessity of processing large amounts of data, biomedical Named Entity Recognition (NER) has become an essential technique for information extraction in the biomedical field. In this work, we present a technically simple architecture for Biomedical Named Entity Recognition (BNER) using CRF algorithm. The proposed system uses clustering based word representation and word embedding based word representation as the only features along with the basic feature set to identify the entities. In addition, we incorporated BIO entity tag representation to effectively identify the biomedical entities. The proposed system is evaluated on two publicly available dataset BioCreAtIvE II GM corpus and JNLPBA corpus. The experimental results on both the corpus shows that the system outperforms all the existing system with the relatively high F - score of 85.92 % on BioCreAtIvE II GM corpus and 76.64 % on JNLPBA.