As supervised machine learning methods are increasingly used in language technology, the need for high-quality annotated language data becomes imminent. Active learning (AL) is a means to alleviate the burden of annotation. This paper addresses the problem of knowing when to stop the AL process without having the human annotator make an explicit deci-sion on the matter. We propose and evaluate an intrinsic criterion for committee-based AL of named entity recognizers.
Abstract Background Active learning (AL) has shown the promising potential to minimize the annotatio...
In natural language acquisition, it is difficult to gather the annotated data needed for supervised ...
A common obstacle preventing the rapid deployment of supervised machine learning algorithms is the l...
As supervised machine learning methods are increasingly used in language technology, the need for hi...
In this paper, we address the problem of knowing when to stop the process of active learning. We pro...
Active learning is a supervised machine learning technique in which the learner is in control of the...
In natural language acquisition, it is di#- cult to gather the annotated data needed for supervise...
In lots of natural language processing tasks, the classes to be dealt with often occur heavily imbal...
A survey of existing methods for stopping active learning (AL) reveals the needs for methods that ar...
Within the natural language processing (NLP) community, active learning has been widely investigated...
Abstract. Manual annotation is a tedious and time consuming process, usually needed for generating t...
A survey of existing methods for stopping ac-tive learning (AL) reveals the needs for meth-ods that ...
Active learning reduces annotation costs for supervised learning by concentrating labelling efforts ...
In recent decades, the availability of a large amount of data has propelled the field of machine lea...
As supervised machine learning methods for addressing tasks in natural language process-ing (NLP) pr...
Abstract Background Active learning (AL) has shown the promising potential to minimize the annotatio...
In natural language acquisition, it is difficult to gather the annotated data needed for supervised ...
A common obstacle preventing the rapid deployment of supervised machine learning algorithms is the l...
As supervised machine learning methods are increasingly used in language technology, the need for hi...
In this paper, we address the problem of knowing when to stop the process of active learning. We pro...
Active learning is a supervised machine learning technique in which the learner is in control of the...
In natural language acquisition, it is di#- cult to gather the annotated data needed for supervise...
In lots of natural language processing tasks, the classes to be dealt with often occur heavily imbal...
A survey of existing methods for stopping active learning (AL) reveals the needs for methods that ar...
Within the natural language processing (NLP) community, active learning has been widely investigated...
Abstract. Manual annotation is a tedious and time consuming process, usually needed for generating t...
A survey of existing methods for stopping ac-tive learning (AL) reveals the needs for meth-ods that ...
Active learning reduces annotation costs for supervised learning by concentrating labelling efforts ...
In recent decades, the availability of a large amount of data has propelled the field of machine lea...
As supervised machine learning methods for addressing tasks in natural language process-ing (NLP) pr...
Abstract Background Active learning (AL) has shown the promising potential to minimize the annotatio...
In natural language acquisition, it is difficult to gather the annotated data needed for supervised ...
A common obstacle preventing the rapid deployment of supervised machine learning algorithms is the l...