The application of Kalman filter based human-computer learning model to Chinese word segmentation

Abstract

This paper presents a human-computer interaction learning model for segmenting Chinese texts depending upon neither lexicon nor any annotated corpus. It enables users to add language knowledge to the system by directly intervening the segmentation process. Within limited times of user intervention, a segmentation result that fully matches the use (or with an accurate rate of 100% by manual judgement) is returned. A Kalman filter based model is adopted to learn and estimate the intention of users quickly and precisely from their interventions to reduce system prediction error hereafter. Experiments show that it achieves an encouraging performance in saving human effort and the segmenter with knowledge learned from users outperforms the baseline model by about 10% in segmenting homogenous texts. © 2013 Springer-Verlag.