The amount of text that is generated every day is increasing dramatically. This tremendous
volume of mostly unstructured text cannot be simply processed and perceived by computers …

Textual information is becoming available in abundance on the web, arising the requirement
of techniques and tools to extract the meaningful information. One of such an important …

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing,
concerned with identifying spans of text expressing references to entities. NER research is …

Natural language processing (NLP) is useful for handling text and speech, and sequence
labeling plays an important role by automatically analyzing a sequence (text) to assign …

The number of scientific publications in the literature is steadily growing, containing our
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …

We propose a simple deep neural model for nested named entity recognition (NER). Most
NER models focused on flat entities and ignored nested entities, which failed to fully capture …

Efficient access to chemical information contained in scientific literature, patents, technical
reports, or the web is a pressing need shared by researchers and patent attorneys from …

Introduction e term “Named Entity”, now widely used in Natural Language Processing, was
coined for the Sixth Message Understanding Conference (MUC-6)(R. Grishman & Sundheim …

With the rapid development of text mining, many studies observe that text generally contains
a variety of implicit information, and it is important to develop techniques for extracting such …

In natural language processing, it is common that many entities contain other entities inside
them. Most existing works on named entity recognition (NER) only deal with flat entities but …