Motivation: Text mining is increasingly used to manage the accelerating pace of the
biomedical literature. Many text mining applications depend on accurate named entity …

Motivation Recognition of biomedical named entities in the textual literature is a highly
challenging research topic with great interest, playing as the prerequisite for extracting huge …

Background Biomedical named entity recognition (BioNER) is an important task for
understanding biomedical texts, which can be challenging due to the lack of large-scale …

Identifying the interactions between chemical compounds and genes from biomedical
literatures is one of the frequently discussed topics of text mining in the life science field. In …

The article describes a knowledge-poor approach to the task of extracting Chemical-
Disease Relations from PubMed abstracts. A first version of the approach was applied …

There have been an increasing number of various machine learning-based models
successfully proposed and applied for automatic chemical-induced disease (CID) relation …

This paper proposes an effective and robust approach for Chemical-Induced Disease (CID)
relation extraction from PubMed articles. The study was performed on the Chemical Disease …

Medical entity extraction (EE) is a standard procedure used as a first stage in medical texts
processing. Usually Medical EE is a two-step process: named entity recognition (NER) and …

The BioCreative V chemical-disease relation (CDR) track was proposed to accelerate the
progress of text mining in facilitating integrative understanding of chemicals, diseases and …

Biomedical relations are often expressed between entities occurring within the same
sentence through syntactic means. However, a significant portion of such relations (in …