Simple Semantic Annotation and Situation Frames: Two Approaches to Basic Text Understanding in LORELEI

Abstract

We present two types of semantic annotation developed for the DARPA Low Resource Languages for Emerging Incidents (LORELEI) program: Simple Semantic Annotation (SSA) and Situation Frames (SF). Both of these annotation approaches are concerned with labeling basic semantic information relevant to humanitarian aid and disaster relief (HADR) scenarios, with SSA serving as a more general resource and SF more directly supporting the evaluation of LORELEI technology. Mapping between information in different annotation tasks is an area of ongoing research for both system developers and data providers. We discuss the similarities and differences between the two types of LORELEI semantic annotation, along with ways in which the general semantic information captured in SSA can be leveraged in order to recognize HADR-oriented information captured by SF. To date we have produced annotations for nineteen LORELEI languages; by the program’s end both SF and SSA will be available for over two dozen typologically diverse languages. Initially data is provided to LORELEI performers and to participants in NIST’s Low Resource Human Language Technologies (LoReHLT) evaluation series. After their use in LORELEI and LoReHLT evaluations the data sets will be published in the LDC catalog.

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