Recently, neural models have shown superior performance over conventional models in
NER tasks. These models use CNN to extract sub-word information along with RNN to …

With rapid development of the Internet, people have undergone tremendous changes in the
way they obtain information. In recent years, knowledge graph is becoming a popular tool for …

Maintenance records of complex industrial equipment contain a large amount of
unstructured data (eg technician notes) pertaining to repair actions and associated …

In the medical domain and other scientific areas, it is often important to recognize different
levels of hierarchy in mentions, such as those related to specific symptoms or diseases …

Liner2 a Generic Framework for Named Entity Recognition Page 1 Proceedings of the 6th
Workshop on Balto-Slavic Natural Language Processing, pages 86–91, Valencia, Spain, 4 …

In recent years, a huge amount of text information requires processing to support the
diagnosis and treatment of diabetes in the medical field; therefore, the named entity …

In the field of ancient Chinese text, extracting and analysing temporal and geographic
information are crucial for understanding the personal experiences of historical figures, the …

In this article we discuss the current state-of-the-art for named entity recognition for Polish.
We present publicly available resources and open-source tools for named entity recognition …

Uyghur is a morphologically rich and typical agglutinating language, and morphological
segmentation affects the performance of Uyghur named-entity recognition (NER). Common …

Named entity recognition (NER) is the process of categorizing named entities in a given text
that suffers from the lack of labeled corpora, which is a long-standing issue. Deep neural …