Online Inference for Relation Extraction with a Reduced Feature Set
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Access to web-scale corpora is gradually bringing robust automatic knowledge base creation and extension within reach. To exploit these large unannotated---and extremely difficult to annotate---corpora, unsupervised machine learning methods are required. Probabilistic models of text have recently found some success as such a tool, but scalability remains an obstacle in their application, with standard approaches relying on sampling schemes that are known to be difficult to scale. In this report, we therefore present an empirical assessment of the sublinear time sparse stochastic variational inference (SSVI) scheme applied to RelLDA. We demonstrate that online inference leads to relatively strong qualitative results but also identify some of its pathologies---and those of the model---which will need to be overcome if SSVI is to be used for large-scale relation extraction.
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