Biblio

Distant supervision is a widely used data labeling method for relation extraction. While aligning knowledge base with the corpus, distant supervision leads to a mass of wrong labels which are defined as noise. The pattern-based denoising model has achieved great progress in selecting trustable sentences (instances). However, the writing of relation-specific patterns heavily relies on expert’s knowledge and is a high labor intensity work. To solve these problems, we propose a noise reduction framework, NOIR, to iteratively select trustable sentences with a little help of a human. Under the guidance of experts, the iterative process can avoid semantic drift. Besides, NOIR can help experts discover relation-specific tokens that are hard to think of. Experimental results on three real-world datasets show the effectiveness of the proposed method compared with state-of-the-art methods.

Recommendation based on heterogeneous information network(HIN) is attracting more and more attention due to its ability to emulate collaborative filtering, content-based filtering, context-aware recommendation and combinations of any of these recommendation semantics. Random walk based methods are usually used to mine the paths, weigh the paths, and compute the closeness or relevance between two nodes in a HIN. A key for the success of these methods is how to properly set the weights of links in a HIN. In existing methods, the weights of links are mostly set heuristically. In this paper, we propose a Bayesian Personalized Ranking(BPR) based machine learning method, called HeteLearn, to learn the weights of links in a HIN. In order to model user preferences for personalized recommendation, we also propose a generalized random walk with restart model on HINs. We evaluate the proposed method in a personalized recommendation task and a tag recommendation task. Experimental results show that our method performs significantly better than both the traditional collaborative filtering and the state-of-the-art HIN-based recommendation methods.