基于词典信息的先秦汉语全文词义标注方法研究

词义消歧是自然语言处理中的一项基础任务,古汉语信息处理也急需深层次的语义标注工作。该文针对先秦古汉语这一特殊的语言材料,在训练语料和语义资源匮乏的条件下,采用《汉语大词典2.0》作为知识来源,将其词条释义作为义类,每个义项的例句作为训练语料,使用基于支持向量机(SVM)的半指导方法对《左传》进行全文的词义标注。按照频度不同、义项数量不同的原则,我们随机选取了22个词进行了人工检查,平均正确率达到67%。该方法可以广泛用于缺乏训练语料的古汉语义项标注工作,能够在古汉语全文词义标注的起步阶段提供初始结果,为人工标注词语义项提供良好的数据底本,补正传统词典释义不全的问题,进一步丰富汉语史发展研究资料。

A Study in Dictionary-Based All-word Word Sense Disambiguation for Pre-Qin Chinese

Word Sense Disambiguation(WSD) is a basic task of Natural Language Processing,including the processing of ancient Chinese documents.In this paper we focuse on the specific field of analyzing pre-Qin ancient Chinese documents.Considering the shortage of training data and semantic resources,we employe a semi-supervised machine learning method to perform all-word WSD of Zuo Zhuan and use Chinese Dictionary v2.0 as the knowledge resource.We randomly selecte 22 words of different frequency and sense number to evaluate the proposed method.On the selected words,our method achieves an average accuracy of 67%,which is significant higher than the baseline method of selecting the most frequent sense.This method is promising for sense tagging of ancient Chinese documents when there is no training data available.It also provides a raw sense tagging result for human correction,enriching traditional dictionaries which usually suffer from insufficient word sense entries.