Abstract Biomedical Text Mining targets the Extraction of significant information from
biomedical archives. Bio TM encompasses Information Retrieval (IR) and Information …

The paper presents a novel application to extract biomedical entities automatically using
machine learning techniques from large volumes of biomedical text. The data in large …

Developments in advanced innovations have prompted the generation of an immense
amount of digital information. The data deluge contains hidden information that is difficult to …

The biomedical society makes wide use of text mining technology. Named Entity (NE)
extraction is one of the most primary and significant tasks in biomedical information …

The primary goal of text mining is to retrieve knowledge that is hidden in text and to present it
in a concise and simple form to the final users. To achieve this objective, two main directions …

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 …

There has been growing work in the field of Named Entity Recognition (NER) and a lot of
research has been done in this direction in last two decades. Particularly, a lot of progress …

With high-throughput technologies applied in biomedical research, the quantity of
biomedical literatures grows exponentially. It becomes more and more important to quickly …

The amount of biomedical textual information available in the web becomes more and more.
It is very difficult to extract the right information that users are interested in considering the …

Named Entity Recognition and Classification (NERC) is an important task in information
extraction for biomedicine domain. Biomedical Named Entities include mentions of proteins …