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 amount of high-valued biomedical knowledge deposited in unstructured text and transforming them into well-structured formats. Long Short-Term Memory (LSTM) networks have recently been employed in various biomedical named entity recognition (NER) models with great success. They, however, often did not take advantages of all useful linguistic information and still have many aspects to be further improved for better performance.