The thesis defines and evaluates tasks and datasets for automatic fact checking, cite-worthiness, exaggeration detection, and information change measurement in science communication, culminating in SPICED, a cross-media dataset of semantically matched scientific findings.
Crowdsourcing Semantic Label Propagation in Relation Classification
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abstract
Distant supervision is a popular method for performing relation extraction from text that is known to produce noisy labels. Most progress in relation extraction and classification has been made with crowdsourced corrections to distant-supervised labels, and there is evidence that indicates still more would be better. In this paper, we explore the problem of propagating human annotation signals gathered for open-domain relation classification through the CrowdTruth methodology for crowdsourcing, that captures ambiguity in annotations by measuring inter-annotator disagreement. Our approach propagates annotations to sentences that are similar in a low dimensional embedding space, expanding the number of labels by two orders of magnitude. Our experiments show significant improvement in a sentence-level multi-class relation classifier.
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Machine Understanding of Scientific Language
The thesis defines and evaluates tasks and datasets for automatic fact checking, cite-worthiness, exaggeration detection, and information change measurement in science communication, culminating in SPICED, a cross-media dataset of semantically matched scientific findings.