Fast and Large-scale Unsupervised Relation Extraction

A common approach to unsupervised relation extraction builds clusters of patterns express-ing the same relation. In order to obtain clus-ters of relational patterns of good quality, we have two major challenges: the semantic rep-resentation of relational patterns and the scal-ability to large data. In this paper, we ex-plore various methods for modeling the mean-ing of a pattern and for computing the similar-ity of patterns mined from huge data. In order to achieve this goal, we apply algorithms for approximate frequency counting and efficient dimension reduction to unsupervised relation extraction. The experimental results show that approximate frequency counting and dimen-sion reduction not only speeds up similarity computation but also improves the quality of pattern vectors.