Automatic Semantic Role Labeling

Wen-tau Yih, Kristina Toutanova
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引用次数: 9

Abstract

The goal of semantic role labeling is to map sentences to domain-independent semantic representations, which abstract away from syntactic structure and are important for deep NLP tasks such as question answering, textual entailment, and complex information extraction. Semantic role labeling has recently received significant interest in the natural language processing community. In this tutorial, we will first describe the problem and history of semantic role labeling, and introduce existing corpora and other related tasks. Next, we will provide a detailed survey of state-of-the-art machine learning approaches to building a semantic role labeling system. Finally, we will conclude the tutorial by discussing directions for improving semantic role labeling systems and their application to other natural language problems.
自动语义角色标注
语义角色标注的目标是将句子映射到与领域无关的语义表示,这些语义表示从句法结构中抽象出来,对于深度NLP任务(如问答、文本蕴涵和复杂信息提取)非常重要。语义角色标注最近在自然语言处理领域引起了极大的兴趣。在本教程中,我们将首先描述语义角色标注的问题和历史,并介绍现有的语料库和其他相关任务。接下来,我们将提供最先进的机器学习方法来构建语义角色标记系统的详细调查。最后,我们将通过讨论改进语义角色标记系统及其在其他自然语言问题中的应用方向来结束本教程。
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