In this paper, we revisited the topic of sentence boundary detection, and proposed an incremental approach to tackle the problem. The boundary classifier is revised on the fly to adapt to the text of high variety of sources and genres. We applied i + Learning, an incremental algorithm, for constructing the sentence boundary detection model using different features based on local context. Although the model can be easily trained on any genre of text and on any alphabet language, we emphasize the ability that the classifier is adaptable to text with domain and topic shifts without retraining the whole model from scratch. Empirical results indicate that the performance of proposed system is comparable to that of similar systems.
['Fai Wong', 'Sam Chao']
iSentenizer: An incremental sentence boundary classifier