In this paper, we propose a learning classifier based on maximum entropy (ME) for resolving zero-anaphora in Chinese text. Besides regular grammatical, lexical, positional and semantic features motivated by previous research on anaphora resolution, we develop two innovative Web-based features for extracting additional semantic information from the Web. The values of the two features can be obtained easily by querying the Web using some patterns. Our study shows that our machine learning approach is able to achieve an accuracy comparable to that of state-of-the-art systems. The Web as a knowledge source can be incorporated effectively into the ME learning framework and significantly improves the performance of our approach.

In this paper, an improved GPS/RFID integration method based on Sequential Iterated Reduced Sigma Point Kalman Filter (SIRSPKF) is proposed for vehicle navigation applications. It is applied to improve the accuracy, reliability and availability of satellite positioning in the areas where the satellite visibility is limited. An RFID system is employed to assist the GPS system in achieving high accuracy positioning. Further, to reduce the measurement noise and decrease the computational complexity caused by the integrated GPS/RFID, SIRSPKF is investigated as the dominant filter for the proposed integration. Performances and computational complexities of different integration scenarios with different filters are compared in this paper. A field experiment shows that both accuracy and availability of positioning can be improved significantly by this low-cost GPS/RFID integration method with the reduced computational load.