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Author Roth, Dan ♦ Small, Kevin
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Corresponding High Cost ♦ Pipeline Model ♦ Unlabeled Example ♦ Relation Extraction ♦ Textual Entailment ♦ Second Feature ♦ Machine Learning ♦ New Algorithm ♦ Pipelined Model ♦ Common Practice ♦ Single Classifier ♦ Semantic Role Labeling ♦ Complex Task ♦ Local Classifier ♦ Single Classification Task ♦ Many Task ♦ Pipelined Process ♦ Active Learning Protocol ♦ Complex Classification Task ♦ Natural Language Processing Community ♦ Active Learning ♦ Sequential Stage ♦ Input Text ♦ Active Learning Research ♦ Domain Expert ♦ Previous Stage ♦ Primary Motivation ♦ Significant Recent Attention ♦ Active Learning Algorithm ♦ Promising Solution
Description Decomposing complex classification tasks into a series of sequential stages, where the local classifier at each stage is explicitly dependent on predictions from previous stages, is a common practice. In the machine learning and natural language processing communities, this widely used paradigm is generally referred to as a pipeline model. This approach has been successfully applied to many tasks, including parsing, semantic role labeling, and textual entailment [3]. The primary motivation for modeling complex tasks as a pipelined process is the difficulty of solving such applications with a single classifier; that learning a classifier for a problem such as relation extraction directly in terms of input text may be impossible with the given resources. A second feature of domains requiring such decompositions is the corresponding high cost associated with obtaining sufficient labeled data. The active learning protocol offers one promising solution to this dilemma by allowing the learning algorithm to incrementally select unlabeled examples for labeling by the domain expert with the goal of maximizing performance while minimizing supervision [1]. While receiving significant recent attention, most active learning research focuses on new algorithms as they relate to a single classification task. This work instead assumes that an active learning algorithm exists for each stage of a pipelined model and develops
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Institution In Proceedings of the National Conference on Artificial Intelligence. 683–688