The key idea behind active learning is that a machine learning algorithm can achieve
greater accuracy with fewer labeled training instances if it is allowed to choose the training …

In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …

Learning of classifiers to be used as filters within the analytical reasoning process leads to
new and aggravates existing challenges. Such classifiers are typically trained ad-hoc, with …

Neural coreference resolution models trained on one dataset may not transfer to new, low-
resource domains. Active learning mitigates this problem by sampling a small subset of data …

We improve upon pairwise annotation for active learning in coreference resolution, by
asking annotators to identify mention antecedents if a presented mention pair is deemed not …

Active learning has been proven a reliable strategy to reduce manual efforts in training data
labeling. Such strategies incorporate the user as oracle: the classifier selects the most …

In this paper, we define the problem of coreference resolution in text as one of clustering
with pairwise constraints where human experts are asked to provide pairwise constraints …

The increasing availability of pen-based tablets, and pen-based interfaces opened the
avenue for computer graphics applications that can utilize sketch recognition technologies …

Active learning can lower the cost of annotation for some natural language processing tasks
by using a classifier to select informative instances to send to human annotators. It has …

In the literature, most prior work on coreference resolution centered on the newswire
domain. Although a coreference resolution system trained on the newswire domain performs …