- Benjamin Roth, Nicholas Monath, David Belanger, Emma Strubell, Pat Verga, A. McCallum. 2015. Building Knowledge Bases with Universal Schema: Cold Start and Slot-Filling Approaches. Abstract: We compare the performance of two different relation prediction architectures based on the same relation predictors. The knowledge base construction architecture builds a complete knowledge base for the entire corpus, and commits to entity linking and clustering decisions ahead of time. The query-driven slot filling architecture can make entity expansion and retrieval decisions on the fly, and has the flexibility to trade precision for recall. We use a wide range of established and novel techniques for our relation extraction components. They include distant supervision-based classifiers (SVM and convolutional neural nets), rule-based extractors, and semisupervised matrix embedding methods taking into account all co-occurrences of surface patterns and entities in the corpus (universal schema).
