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POSID: Target-Task-Unsupervised Person-Attribute Extraction from Heterogeneous Public-Safety Text
Person descriptions in public-safety text can be compact and regular, or distributed across heterogeneous narratives in which evidence boundaries and person ownership are ambiguous. We present POSID, a target-task-unsupervised framework that decomposes person-attribute extraction into candidate loca
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