Syntactic Processing Using the Generalized Perceptron and Beam Search
Syntactic Processing Using the Generalized Perceptron and Beam Search
复制标题
使用广义感知器和束搜索进行句法处理
DOI：
10.1162/coli_a_00037
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发表时间：
2011
影响因子：
9.3
通讯作者：
Zhang Y
中科院分区：
文献类型：
--
作者：
Zhang Y
我们使用一个通用的统计框架来研究一系列的句法处理任务，该框架由一个全局线性模型组成，由广义感知器和一个通用的波束搜索解码器一起训练。我们将该框架应用于分词、联合切分和词性标注、依存句法分析和短语结构句法分析。该框架的两个组件在概念上和计算上都非常简单。波束搜索解码器只需要将句法处理任务分解成一系列判决，使得在该过程的每个阶段，解码器能够考虑前n个候选并为下一阶段生成所有可能性。一旦解码器被定义，它被应用于训练数据，使用根据广义感知器的微不足道的更新来诱导模型。这种简单的框架性能出人意料地好，在我们考虑的所有任务上提供了与最先进水平相当的精度结果。解码器和训练算法的计算简单性使得测试速度和训练时间显著高于它们的主要替代方案，包括对数线性和大间隔训练算法和用于译码的动态规划。此外，该框架提供了定义任意特征的自由，这可能会使替代训练和解码算法变得令人望而却步。我们讨论了如何将通用框架应用于本文研究的每个问题，并与其他学习和解码算法进行了比较。我们还展示了波束所考虑的候选者的可比性如何是影响性能的一个重要因素。我们认为，该框架在概念和计算上的简单性，以及其独立于语言的性质，使其成为一系列句法处理任务的竞争性选择，并应被替代方法的开发人员考虑进行比较。
We study a range of syntactic processing tasks using a general statistical framework that consists of a global linear model, trained by the generalized perceptron together with a generic beam-search decoder. We apply the framework to word segmentation, joint segmentation and POS-tagging, dependency parsing, and phrase-structure parsing. Both components of the framework are conceptually and computationally very simple. The beam-search decoder only requires the syntactic processing task to be broken into a sequence of decisions, such that, at each stage in the process, the decoder is able to consider the top-n candidates and generate all possibilities for the next stage. Once the decoder has been defined, it is applied to the training data, using trivial updates according to the generalized perceptron to induce a model. This simple framework performs surprisingly well, giving accuracy results competitive with the state-of-the-art on all the tasks we consider.The computational simplicity of the decoder and training algorithm leads to significantly higher test speeds and lower training times than their main alternatives, including log-linear and large-margin training algorithms and dynamic-programming for decoding. Moreover, the framework offers the freedom to define arbitrary features which can make alternative training and decoding algorithms prohibitively slow. We discuss how the general framework is applied to each of the problems studied in this article, making comparisons with alternative learning and decoding algorithms. We also show how the comparability of candidates considered by the beam is an important factor in the performance. We argue that the conceptual and computational simplicity of the framework, together with its language-independent nature, make it a competitive choice for a range of syntactic processing tasks and one that should be considered for comparison by developers of alternative approaches.