基于层次过滤模型的中文指代消解

Chinese anaphora resolution based on multi-pass sieve model

针对现有的中文指代消解研究大多采用二元分类模型,容易出现消解正确率低的特征覆盖消解正确率高的特征以致模型指代划分错误的问题,提出了一种改进的层次过滤模型用于中文指代消解。该模型结合中文语义知识,在原模型中加入语义匹配层,该层通过引入Web语义知识很好地弥补了中文语义知识库较小的不足,并针对中文的特点对原模型的待消解项识别层进行相应的修改使之更加适合中文指代消解。将上述模型与两类基准系统在ACE2005中文语料上进行5种测评,结果表明,所提出模型的F平均值分别高于两类基准系统约4%和9%。

Most existing Chinese anaphora resolution models determine whether two mentions are coreferent by a binary classifier.This approach can lead to incorrect decisions as lower precision features often overwhelm the precision features.We propose a modified multi-pass sieve model for Chinese anaphora resolution to adapt to Chinese.We add a new semantic-based sieve to the original model for incorporating word sense information.The Web word sense information is imported to solve resource constraints.Furthermore,we modify the mention detection sieve based on the Chinese characters.The proposed model is evaluated on five different testing methods on the ACE2005 corpus.Results show that the proposed model outperforms two other baseline models by 4% and 9%respectively.