Liberal Information Extraction: Heng Ji (Rensselaer Polytechnic Institute)

We propose a brand new "Liberal" Information Extraction (IE) paradigm to combine the merits of traditional IE (high quality and fine granularity) and Open IE (high scalability). Liberal IE aims to discover schemas and extract facts from any input corpus, without any annotated training data or predefined schema. Using event extraction as a case study, we present a pilot Liberal IE framework which incorporates symbolic semantics (Abstract Meaning Representation) and distributional semantics to detect and represent rich event structure, and adopts a joint typing framework to simultaneously discover types of events and participants as well as schema which is customized for the input corpus. Experiments demonstrate that Liberal IE can construct high-quality schemas, discover a high proportion of fine-grained typed events in manually defined schemas, achieve comparable performance as supervised models trained from a large amount of labeled data for pre-defined event types, as well as accurately extract many new event types and argument roles. I will also briefly talk about how to extend Liberal IE to multiple data modalities and languages.