Abstract Information Extraction (IE) techniques are essential to gleaning valuable
information about entities and their relationships from unstructured text and creating a …

The ever-evolving volume of digital information requires the development of innovative
search strategies aimed at obtaining the necessary data efficiently and economically …

Named entity recognition (NER) is a task that seeks to recognize entities in raw texts and is a
precondition for a series of downstream NLP tasks. Traditionally, prior NER models use the …

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 …

Named entity recognition (NER) is an important task in the natural language processing
field. Existing NER methods heavily rely on labeled data for model training, and their …

Named entity recognition (NER) is a natural language processing tool for information
extraction from unstructured text data such as e-mails, newspapers, blogs, etc. NER is the …

Learning a Sampling Strategy for Named Entity Recognition Page 1 Learning a Sampling
Strategy for Named Entity Recognition Christopher Kümmel (845567) 1 Page 2 Design …

Abstract Named Entity Recognition (NER) is an information extraction task responsible for
extracting and categorizing entities from unstructured text. Recently, nested NER has been …

Named entity recognition (NER) systems that perform well require task-related and manually
annotated datasets. However, they are expensive to develop, and are thus limited in size. As …

Most existing approaches to named entity recognition (NER) rely on a large amount of
highquality annotations or a more complete specific entity lists. However, in practice, it is …