Transformation Based Learning in the Fast Lane
نویسندگان
چکیده
Transformation-based learning has been successfully employed to solve many natural language processing problems. It achieves state-of-the-art performance on many natural language processing tasks and does not overtrain easily. However, it does have a serious drawback: the training time is often intorelably long, especially on the large corpora which are often used in NLP. In this paper, we present a novel and realistic method for speeding up the training time of a transformation-based learner without sacri cing performance. The paper compares and contrasts the training time needed and performance achieved by our modi ed learner with two other systems: a standard transformation-based learner, and the ICA system (Hepple, 2000). The results of these experiments show that our system is able to achieve a signi cant improvement in training time while still achieving the same performance as a standard transformation-based learner. This is a valuable contribution to systems and algorithms which utilize transformation-based learning at any part of the execution.
منابع مشابه
Numerical Solution of the Lane-Emden Equation Based on DE Transformation via Sinc Collocation Method
In this paper, numerical solution of general Lane-Emden equation via collocation method based on Double Exponential DE transformation is considered. The method converts equation to the nonlinear Volterra integral equation. Numerical examples show the accuracy of the method. Also, some remarks with respect to run-time, computational cost and implementation are discussed.
متن کاملTowards End-to-End Lane Detection: an Instance Segmentation Approach
Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the road lanes, which is also crucial for any subsequent lane departure or trajectory planning decision in fully autonomous cars. Traditional lane detection methods rely on a combination of highly-specialized, hand-crafted...
متن کاملAn Improved Particle Swarm Optimizer Based on a Novel Class of Fast and Efficient Learning Factors Strategies
The particle swarm optimizer (PSO) is a population-based metaheuristic optimization method that can be applied to a wide range of problems but it has the drawbacks like it easily falls into local optima and suffers from slow convergence in the later stages. In order to solve these problems, improved PSO (IPSO) variants, have been proposed. To bring about a balance between the exploration and ex...
متن کاملLife in the Fast Lane: The Evolution of an Adaptive Vehicle Control System
world has been, and continues to be, one of the most difficult tasks in AI research. Since 1987, researchers at Carnegie Mellon University have been investigating one such task. Their research has been focused on using adaptive, vision-based systems to increase the driving performance of the Navlab line of on-road mobile robots. This research has led to the development of a neural network syste...
متن کاملAssessing Behavioral Patterns of Motorcyclists Based on Traffic Control Device at City Intersections by Classification Tree Algorithm
According to the forensic statistics, in Iran, 26 percent of those killed in traffic accidents are motorcyclists in recent years. Thus, it is necessary to investigate the causes of motorcycle accidents because of the high number of motorcyclist casualties. Motorcyclists' dangerous behaviors are among the causes of events that are discussed in this study. Traffic signs have the important role of...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره cs.CL/0107020 شماره
صفحات -
تاریخ انتشار 2001