RI: Small: Semantics-Based, Weakly-Supervised Coreference Resolution

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This research seeks to push the frontiers of coreference resolution research via achieving two objectives. First, it addresses the Winograd Schema Challenge by examining a class of difficult-to-resolve anaphors whose resolution requires commonsense knowledge involving the roles played by the participants in an event and their causal relationships. It adopts a deep text-understanding approach to this problem. Specifically, it ventures into unexplored areas of coreference research, including the use of script-like knowledge and sentiment analysis, as well as an examination of the role of discourse connectives. Second, it enables the acquisition of coreference resolvers for a substantially larger number of natural languages and domains than is currently possible. One of the major obstacles to the deployment of coreference technologies across a large number of languages and domains is the high cost associated with coreference-annotating data in a language and domain. It investigates two cost-effective approaches to data annotation, one involving translation-based projection and the other bootstrapping.As coreference is an enabling technology for many traditional and emerging text-processing applications, the project has the potential to improve these applications. Through the construction of a multi-lingual, multi-domain coreference resolver and the availability of annotated data produced in the course of this investigation, the project may stimulate research in under-studied languages and domains by a broader community of researchers. Equally importantly, the use of commonsense knowledge in the resolver mimics the human coreference resolution process, bringing artificial intelligence researchers one step closer to building an intelligent agent that can truly understand natural language.