2008 CuriousMachinesActiveLearningwi

(Settles, 2008) ⇒ Burr Settles. (2008). “Curious Machines: Active Learning with Structured Instances." PhD. Thesis, University of Wisconsin at Madison. ISBN: 978-1-109-04741-7.

Subject Headings: Active Learning, Named Entity Recognition

Notes

Cited By

Quotes

Abstract

Supervised machine learning is a branch of artificial intelligence concerned with automatically inducing predictive models from labeled data. Such learning approaches are useful for many interesting real-world applications, but particularly shine for tasks involving the automatic organization, extraction, and retrieval of information from large collections of data (e.g., text, images, and other digital media).

In traditional supervised learning, one uses “labeled” training data to induce a model. However, labeled instances for real-world applications are often difficult, expensive, or time consuming to obtain. Consider a complex task such as extracting key person and organization names from text documents. While gathering large amounts of unlabeled documents for these tasks is often relatively easy (e.g., from the World Wide Web), labeling these texts usually requires experienced human annotators with specific domain knowledge and training. There are implicit costs associated with obtaining these labels from domain experts, such as limited time and financial resources. This is especially true for applications that involve learning from instances with complex structures, which can require labels at varying levels of granularity.

Active learning addresses this inherent bottleneck by allowing the learner to selectively choose which parts of the available data are labeled for training. The goal is to maximize the accuracy of the learner through such “queries,” while minimizing the work required of human annotators. In this thesis, I explore several important questions regarding active learning for these and similar tasks involving structured instances. What query strategies are available for these learning algorithms, and how do they compare? How might a learner pose queries at different levels of granularity, as with multiple-instance learning? Are there relationships between certain properties of a query and its difficulty for the annotator? If so, can these relationships be learned and exploited during active learning? The answers to the questions illustrate the utility and promise of active learning algorithms in complex real-world learning systems.

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Thesis Statement

This thesis aims to explore various key aspects of active learning for tasks that involve structured instances. The chapters that follow (i) describe machine learning approaches to various structured learning tasks, (ii) present the active learning scenarios and algorithms I have developed for these learning methods, and (iii) discuss how these approaches can mitigate the amount of work required to acquire labeled data in practice. Specifically, I focus on the following hypotheses:

i. Strategies that take into account how “representative” or “relevant” query instances are can produce more accurate systems with fewer labeled instances than strategies that do not.
ii. When querying instances with complex structures (e.g., labels on individual words in a sentence), strategies that consider the structured instance as a whole can perform better than strategies that aggregate individual label information.
iii. For some structured instances, labels can be acquired at multiple levels of granularity (e.g., documents and paragraphs). By selectively querying at these various granularities, particularly when one is easier to label than another, we can even further reduce annotation effort.
iv. Not all instances have equal annotation cost. To truly minimize the cost of acquiring labeled data, an active learning system should not only consider how informative each query is to the learner, but also take into account how expensive it will be for an annotator to label.

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Active Learning

There are three general scenarios in which active learning is possible: (i) query instances may be synthesized by the learner de novo, (ii) instances are provided in a stream and the learner chooses to query or discard each one sequentially, or (iii) there exists a large pool U of unlabeled data which the learner may examine and select queries from. For many real-world tasks, synthesizing queries de novo can lead to instances that are unnatural or difficult for humans to interpret. For example, Baum and Lang (1992) found that a model learning to recognize handwritten characters generated query images that were not real characters at all, but artificial combinations of existing letters and digits. Therefore, the stream-based and pool-based scenarios are often more realistic. In this thesis, I focus on the pool-based setting, since large repositories of unlabeled texts, images, and the like are usually available for these sorts of problems.

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Biomedical Named Entity Recognition

Named entity recognition (NER) is a subtask of information extraction, focused on finding mentions of various entities that belong to semantic classes of interest. In the biomedical domain, entities of interest are usually references to genes, proteins, cell types, and the like.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 CuriousMachinesActiveLearningwi	Burr Settles			Curious Machines: Active Learning with Structured Instances						2008