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Multiple Instance Learning for Natural Language Tasks

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Many state-of-the-art methods for text and natural-language processing employ supervised learning algorithms. A key obstacle to the application of supervised learning methods, however, is that labeled training instances are usually expensive to acquire. One way around this obstacle, I argue, is to exploit data that can be readily and inexpensively labeled at a coarse level of granularity. Such situations are well suited to multiple-instance learning. In multiple-instance learning, individual instances are not given labels, but instead bags of instances are labeled. Whereas a negative bag is assumed to contain only negative instances, a positive bag need contain only one positive instance.

I will describe the multiple-instance setting, discuss its applicability to natural language tasks, and present several recent results on (i) an empirical comparison of multiple-instance learning to ordinary supervised learning, (ii) a method for learning to combine predictions in a multiple-instance setting, and (iii) active multiple-instance learning.

This talk is part of the NLIP Seminar Series series.

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