When evaluating the top 10 triples with the highest relevance weight, our framework outperforms the best baseline LDA by 4.4% in MAP and by 3.91% in P@N. When evaluating the top 5 triples, our framework performs even better and significantly outperforms the best baseline by 5.87% in MAP and by 17.21% in P@N. To analyze the results further, Ours-S, the simplified version of our model without iterative propagation, outperforms two strong baselines VSM and WE, which indicates the effectiveness of encoding distributional semantics.
the task of document enrichment is proposed to tackle this problem, and has been proved helpful in many NLP tasks such as web search (Pantel and Fuxman, 2011), coreference resolution (Bryl et al., 2010), document cluster (Hu et al., 2009) and entity disambiguation (Sen, 2012).