Aspect extraction aims to extract fine-grained opinion targets from opinion texts. Recent work has shown that the syntactical approach, which employs rules about grammar dependency relations between opinion words and aspects, performs quite well. This approach is highly desirable in practice because it is unsupervised and domain independent. However, the rules need to be carefully selected and tuned manually so as not to produce too many errors. Although it is easy to evaluate the accuracy of each rule automatically, it is not easy to select a set of rules that produces the best overall result due to the overlapping coverage of the rules. In this paper, we propose a novel method to select an effective set of rules. To our knowledge, this is the first work that selects rules automatically. Our experiment results show that the proposed method can select a subset of a given rule set to achieve significantly better results than the full rule set and the existing state-of-the-art CRF-based supervised method.

Elbowing your way through crowds can be slow going, but our ability to weave and dodge through a throng of people comes almost as second nature. For robots, however, this simple task can prove a major obstacle that currently limits their usefulness in public places. But now, a team from Stanford University says it has managed to create droid which is able to navigate down streets without mowing down people walking in the opposite direction, which make them better at making deliveries. The Jackrabbot is a robot designed by a team from Stanford University. It takes its name from the nimble yet shy Jackrabbit, which is often found on the university's campus.