基于句法结构约束的模糊限制信息范围检测

模糊限制信息检测用于区分模糊限制信息与事实信息,提高抽取信息的真实性和可靠性。模糊限制信息范围的界定具有依赖于语义和句法结构的特点,是模糊限制信息检测的一个难点。该文提出一种基于句法结构约束的模糊限制信息范围检测方法,基于依存结构树和短语结构树构建决策树,获取句法结构约束集,用于产生句法结构约束特征,并加入到条件随机域模型中进行模糊限制信息范围检测。实验采用CoNLL-2010共享任务数据集,在标准的模糊限制语标注语料上,获得了70.28%的F值,比采用普通的句法结构特征提高了4.22%。

Hedge Scope Detection Based on Syntactic Structural Constraints

Hedge scope detection is used to distinguish factual information and uncertain information, which could improve the authenticity and reliability in information extraction. Hedge scope detection is a difficult task because of its dependency of the semantic and syntactic structures. In this paper, we propose a hedge scope detection method based on syntactic structural constraints. First, two decision trees are constructed on dependency structure and phrase structure respectively to build the syntactic constraint set. And then the hedge scope detection results based on the syntactic constraint set are used as the syntactic constraint features for Conditional Random Fields (CRF) models. Experiments on the CoNLL-2010 corpus achieve the 70.28% F-score on the golden standard hedge cues, which is 4.22% higher than the system with the common syntactic construction features.
Key wordshedge scope detection; syntactic structural constraints; decision tree; conditional random fields