论文标题
使用Perceptron学习者基于过渡的依赖性解析
Transition-Based Dependency Parsing using Perceptron Learner
论文作者
论文摘要
使用依赖性结构进行句法解析已成为具有许多不同解析模型的自然语言处理中的标准技术，特别是数据驱动的模型，这些模型可以在句法注释的Corpora上进行培训。在本文中，我们使用感知者学习者来解决基于过渡的依赖性解析。我们提出的模型为感知者学习者增添了更多相关功能，优于基线弧标准的解析器。我们击败了麦芽和LSTM解析器的UA。我们还提供了解决非目标树解析的可能方法。
Syntactic parsing using dependency structures has become a standard technique in natural language processing with many different parsing models, in particular data-driven models that can be trained on syntactically annotated corpora. In this paper, we tackle transition-based dependency parsing using a Perceptron Learner. Our proposed model, which adds more relevant features to the Perceptron Learner, outperforms a baseline arc-standard parser. We beat the UAS of the MALT and LSTM parsers. We also give possible ways to address parsing of non-projective trees.
