Extended Distributed Prototypical For Biomedical Named Entity Recognition

Biomedical named entity recognition (bio-ner) is an essential step of biomedical information extraction and biomedical text mining. although, a lot of researches have been made in the design of rule-based and supervised tools for general ner, bio-ner still remains a challenge and an area of active research, as still there is huge difference in f-score of 10 points between general newswire ner and bio-ner. the complex structures of the biomedical entities pose a huge challenge for their recognition. to handle this, this paper explores different effective word representations with support vector machine (svm) to deal with the complex structures of biomedical named entities. first, this paper identifies and evaluates a set of morphological and contextual features with svm learning method for bio-ner. this paper also presents an extended distributed representation word embedding technique (edrwe) for bio-ner. these models are evaluated on widely used standard bio-ner dataset namely genia corpus. experimental results show that edrwe technique improves the overall performance of the bio-ner and outperforms all other representation methods. results analysis shows that the new edrwe is satisfactory and effective for bio-ner especially when only a small-sized data set is available.