We present a novel approach to distant supervision that can alleviate this problem based on the following two ideas: First, we use a factor graph to explicitly model the decision whether two entities are related, and the decisionwhether this relation is mentioned in a given sentence; second, we apply constraint-driven semi-supervision to train this model without any knowledge about which sentences express the relations in our training KB.Expand
We propose a series of generative probabilistic models, broadly similar to topic models, each which generates a corpus of observed triples of entity mention pairs and the surface syntactic dependency path between them.Expand
In this article, we study the problem of Web user profiling, which is aimed at finding, extracting, and fusing the “semantic”-based user profile from the Web.Expand
We present a novel approach to relation extraction that integrates information across documents, performs global inference and requires no labelled text.Expand
We propose a universal schema approach to fine-grained entity type prediction by learning latent vector embeddings from probabilistic matrix factorization, thus avoiding the need for hand-labeled data.Expand
This paper addresses several key issues in extraction and mining of an academic social network: 1) extraction of a researcher social network from the existing Web; 2) integration of the publications from existing digital libraries; 3) expertise search on a given topic; 4) association search between researchers.Expand