Abstract Natural Language Processing (NLP) is very important in modern data processing
taking into consideration different sources, forms and purpose of data as well as information …

A huge amount of textual information is available on Web, Facebook, blogs and Wikipedia,
everyday rising new techniques, algorithms and tools extract the useful information …

There is a huge amount of digitalized medical documents which are valuable for many
applications. The text in a medical document is needed to collect knowledges and …

To extract important concepts (named entities) from clinical notes, most widely used NLP
task is named entity recognition (NER). It is found from the literature that several researchers …

NER which is known as Named Entity Recognition is an application of Natural Language
Processing (NLP). NER is an activity of Information Extraction. NER is a task used for …

Medical Named Entity Recognition (MedNER) is an indispensable task in biomedical text
mining. NER aims to recognize and categorize named entities in scientific literature, such as …

Abstract Named Entity Recognition (NER) task aims to identify named entities from
unstructured text and classify them into corresponding entity types. Existing pretraining …

Biomedical Named Entity Recognition (BioNER) is a crucial step for analyzing Biomedical
texts, which aims at extracting biomedical named entities from a given text. Different …

The Recurrent neural network (RNN) has reported impressive performance on processing
requirements in sequence labeling tasks. RNN can recall previously series knowledge and …

Many named entities usually contain nested entities in biomedical texts. Nested entities
pose challenge to the task of named entity recognition. Traditional methods try to solve the …