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 a fundamental task in text mining and natural
language understanding. Current approaches to NER (mostly based on supervised …

Named Entity Recognition (NER) is a crucial natural language understanding task for many
down-stream tasks such as question answering and retrieval. Despite significant progress in …

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 …

Document-level label consistency is an effective indicator that different occurrences of a
particular token sequence are very likely to have the same entity types. Previous work …

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

Low-resource named entity recognition (NER) aims to identify entity mentions when training
data is scarce. Recent approaches resort to distant data with manual dictionaries for …

In recent years, named entity recognition has always been a popular research in the field of
natural language processing, while traditional deep learning methods require a large …

Abstract Named Entity Recognition (NER) is a machine learning task that traditionally relies
on supervised learning and annotated data. Acquiring such data is often a challenge …

The success of neural network based methods in named entity recognition (NER) is heavily
relied on abundant manual labeled data. However, these NER methods are unavailable …