【摘 要】
：
Word segmentation problem is widely solved as the sequence labeling problem.The traditional way to this kind of problem is ma-chine learning method like conditional random field with hand-crafted feat
【机 构】
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Research Center for Social Computing and Information Retrieval Harbin Institute of Technology,China
【出 处】
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第十六届全国计算语言学学术会议暨第五届基于自然标注大数据的自然语言处理国际学术研讨会
论文部分内容阅读
Word segmentation problem is widely solved as the sequence labeling problem.The traditional way to this kind of problem is ma-chine learning method like conditional random field with hand-crafted features.Recently,deep learning approaches have achieved state-of-the-art performance on word segmentation task and a popular method of them is LSTM networks.This paper gives a method to introduce numer-ical statistics-based features counted on unlabeled data into LSTM net-works and analyzes how it enhances the performance of word segmenta-tion model.We add pre-trained character-bigram embedding,pointwise mutual information,accessor variety and punctuation variety into our model and compare their performances on different datasets including three datasets from CoNLL-2017 shared task and three datasets of sim-plified Chinese.We achieve the state-of-the-art performance on two of them and get comparable results on the rest.
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