依存句法解析《Structured Training for Neural Network Transition-Based Parsing》,程序员大本营,技术文章内容聚合第一站。
transition-based parsingEnglishIndonesianVietnameseThis paper introduces an effective method for improving dependency parsing which is based on a graph embedding model. The model helps extract local and global connectivity patterns between tokens. This method allows neural network models to perform better ...
接下来解释什么叫transition-based。依旧是论文的例子。图: parser有以下几项: 一个stackS,root第一个入栈,栈顶靠右,图中栈顶是词good。 一个bufferB,第一个元素是在最左边,按照句子的顺序开始。 一个放dependency边的集合A。 transition的集合 在本文中,有如下几种transition: LEFT-ARC(l): 往A中增加一条s1...
已经有很多改进的方法,比如添加字符级别的嵌入,加入attention机制等等,第二种是一个目前我还未读到的一个模型,Transition-Based Chunking Model,它使用类似于基于过渡的依赖解析(transition-based dependency parsing)的算法来分块和标记输入序列,它构造了多个令牌名称。
Dependency Parsing 主要有两种方法:Transition-based和 Graph-based。 DeepBiaffine Attentionfor NeuralDependency Parsing 基于图的依存句法分析从左向右解析句子,针对句中的每个词,找该词的head词(该词到head词之间的arc)以及从该词到head词之间的依存关系类型,即需要解决两个问题:哪两个节点连依存弧以及弧的标签是...
依存树解析任务目前有两种做法,一是Transition-based approach, 另一种就是graph-based方法;针对每种方法文中给出了将一句话解析成依存书的具体实现步骤,本文的方法是用的graph-based框架。 本文的框架图: graph-based方法: 从左向右解析句子,针对句中的每个词,找该词的head词(该词到head词之间的arc)以及从该词...
(1) Transition-Based:定义actions,在当前state,选择不同的action进入不同的下一个state (2) Graph-Based: 关键是score function 构建树的过程中有三种解码算法: (1) dynamic programming {Three new probabilistic models for dependency parsing: An exploration} ...
Dyer C, Ballesteros M, Ling W, Matthews A, Smith NA (2015) Transition-based dependency parsing with stack long short-term memory. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, ...
Transition-based dependency parsing with stack long short-term memory. Comput Sci. 2015;37(2):321–32. Google Scholar Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80. Article CAS PubMed Google Scholar Graves A, Mohamed AR, Hinton G. Speech ...
Dyer, N.A. Smith, Improved Transition-based Parsing by Modeling Characters Instead of Words with LSTMs. Available from: <1508.00657>. Google Scholar [44] A. Graves, S. Fernández, J. Schmidhuber Bidirectional LSTM networks for improved phoneme classification and recognition International Conference ...