Majority of information nowadays is carried by massive and unstructured text, in the form of
news, articles, reports, or social media messages. This poses a major research challenge on
mining entity/relation structures from unstructured text. Manual curation or labeling cannot
be scalable to match the rapid growth of text. Most existing information extraction
approaches rely on heavy human annotations, which can be too expensive to tune and not
adaptable to new domains.
In this talk, I will present a pattern-based methodology that conducts information extraction
from the massive corpora using existing resources with little human effort. The first
component, WW-PIE, discovers meaningful textual patterns that contain the entities of
interest. The second component, TruePIE, discovers high quality textual patterns for target
relation types. I will demonstrate how semi-supervised methods can empower information
extraction for broad applications and provide explainable results.
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