We conducted a scoping review for active learning in the domain of natural language
processing (NLP), which we summarize in accordance with the PRISMA-ScR guidelines as …

We consider the automatic synthesis of an entity extractor, in the form of a regular
expression, from examples of the desired extractions in an unstructured text stream. This is a …

Active learning (AL) consists of asking human annotators to annotate automatically selected
data that are assumed to bring the most benefit in the creation of a classifier. AL allows to …

In recent years, many NLP systems and tasks are developed using machine learning
methods. In order to achieve the best performance, these systems are generally trained on a …

We present a variation of the corpus-based entity set expansion and entity list completion
task. A user-specified query and a sentence containing one seed entity are the input to the …

We consider the long-standing problem of the automatic generation of regular expressions
for text extraction, based solely on examples of the desired behavior. We investigate several …

We address the problem of entity extraction with a very few examples and address it with an
information retrieval approach. Existing extraction approaches consider millions of features …

The cost of manually annotating corpora is one of the significant issues in many text based
tasks such as text mining, semantic annotation and generally information extraction. Active …

We consider the long-standing problem of the automatic generation of regular expressions
for text extraction, based solely on examples of the desired behavior. We investigate several …

Supervised learning requires a lot of annotated data, which makes the annotation process
time-consuming and expensive. Active Learning (AL) offers a promising solution by reducing …