【摘要】 框架排歧指的是在一个给定的句子中,判断句中目标词激起的语义场景与该目标词可能激起的哪个框架一致,则将该框架分配给当前的目标词。框架排歧最重要的一个步骤就是特征选择,目前常用的方法是人工特征选择方法,但是这种方法不能有效地利用每个目标词的语义特征,而且大量实验表明,不同的目标词取得最好的结果时所用的特征模板是不同的。因此,该文为每个目标词设置一个特征模板,并提出了特征模板的自动选择算法,首先从语料中抽取特征构成特征集,然后利用打分机制,把特征集中得分最高的特征逐个加入到特征模板中,直到相邻两次的得分不再增加。该文借助汉语框架网语义资源,利用最大熵模型建模,使用自动特征选择算法选出特征模板,并进行5-fold交叉验证,平均精确率可达到84.46%。更多还原

【Abstract】 Frame disambiguation aims to assign appropriate frame for the target words,according to the consistency between semantic scene and the candidate frame evoked by the target words.The key step of frame disambiguation is the feature selection,which is currently a manual process.However,this manual method doesn’t effectively use the semantic feature of each target word.In addition,it is proved that the feature templates are different when the target words achieve best results.Hence,this paper proposes an automatic feature template algorithm to set a feature template for each target word.First,feature sets are composed of features from the corpus;Then the feature achieved the highest score is added to the feature template until the adjacent two score no longer increases.The paper applies a maximum entropy model to Chinese FrameNet corpus,examining the automatic feature selection algorithm by 5-fold cross validation,and achieves an average precision of 84.46%.更多还原