论文标题
语义角色标记作为句法依赖性解析
Semantic Role Labeling as Syntactic Dependency Parsing
论文作者
论文摘要
我们将（基于跨度的）Propbank式语义角色标签（SRL）的任务减少到句法依赖性解析。我们的方法是由我们的经验分析激励的，该分析显示了三种常见的句法模式，占英语和中文数据的SRL注释的98％以上。基于此观察结果，我们提出了一种转换方案，该方案通过关节标签将SRL注释包装到依赖树表示中，该标签允许高度准确地恢复到原始格式。这种表示使我们能够培训统计依赖解析器，以应对SRL并以当前的状态实现竞争性能。我们的发现表明，句法依赖树在编码语义角色关系的句法领域中的诺言依赖树的希望，并指出了将来的句法方法的潜在进一步整合到语义角色标记中。
We reduce the task of (span-based) PropBank-style semantic role labeling (SRL) to syntactic dependency parsing. Our approach is motivated by our empirical analysis that shows three common syntactic patterns account for over 98% of the SRL annotations for both English and Chinese data. Based on this observation, we present a conversion scheme that packs SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format. This representation allows us to train statistical dependency parsers to tackle SRL and achieve competitive performance with the current state of the art. Our findings show the promise of syntactic dependency trees in encoding semantic role relations within their syntactic domain of locality, and point to potential further integration of syntactic methods into semantic role labeling in the future.
