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The recognition of biomedical entities in natural text is a key step for automatic analysis of textual resources. Biomedical entities recognition tools are a prerequisite for many applications working on text. In this paper, we develop a biomedical entities recognition tool named ATRMiner. Using natural language processing technology, ATRMiner can identify five classes biomedical entities, namely...
With the rapid advancement of biomedical science, the extraction of various types of information from biomedical literatures has become a major challenge. In this paper, we develop a system to extract the relationship both gene2disease and gene2gene using a combined approach including pattern, ontology-based and co-occurrence technology. Our system can easily find the relevant genes related to autism...
Biomedical named entity recognition, an important step, makes preparation for extracting information from biomedical textual resources. This paper presents a hybrid approach to recognize biomedical entity, which includes POS (Part-of-Speech) tagging, rules-based and dictionary-based approach using biomedical ontology. Experiment results show our approach can find untagged biomedical entity name in...
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