What Information is Helpful for Dependency Based Semantic Role Labeling
What Information is Helpful for Dependency Based Semantic Role Labeling
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发表时间：
2013-10
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通讯作者：
Yanyan Luo;Kevin Duh;Yuji Matsumoto
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作者：
Yanyan Luo;Kevin Duh;Yuji Matsumoto
语义角色标注（SRL）是一项重要的任务，因为它有利于广泛的自然语言处理应用。给定一个句子，SRL的任务是识别谓词（目标动词或名词）的论元，并为它们分配语义上有意义的标签。基于依赖分析的方法在SRL中取得了很大的成功。然而，由于依赖分析中的错误，基于Oracle分析的SRL和基于自动分析的SRL在实际应用中存在很大的性能差距。有鉴于此，本文探讨了什么额外的信息是必要的，以弥补这一差距。是否值得以N-best解析特征的形式引入额外的依赖信息，或者更好地结合正交非依赖信息（基本块成分）？我们在一个SRL系统中比较了上述特征，该系统在CoNLL 2009中文任务语料库上取得了最新的结果。我们的研究结果表明，正交信息的成分形式是更有助于提高依赖性为基础的SRL在实践中。
Semantic Role Labeling (SRL) is an important task since it benefits a wide range of natural language processing applications. Given a sentence, the task of SRL is to identify arguments for a predicate (target verb or noun) and assign semantically meaningful labels to them. Dependency parsing based methods have achieved much success in SRL. However, due to errors in dependency parsing, there remains a large performance gap between SRL based on oracle parses and SRL based on automatic parses in practice. In light of this, this paper investigates what additional information is necessary to close this gap. Is it worthwhile to introduce additional dependency information in the form of N-best parse features, or is it better to incorporate orthogonal nondependency information (base chunk constituents)? We compare the above features in a SRL system that achieves state-of-theart results on the CoNLL 2009 Chinese task corpus. Our findings suggest that orthogonal information in the form of constituents is much more helpful in improving dependency based SRL in practice.