Smooth Technologies in Head-Driven Parsing

YUAN Li-chi

doi:10.3969/j.issn.0372-2112.2013.07.015

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Smooth Technologies in Head-Driven Parsing

更新时间：2025-07-16

- Smooth Technologies in Head-Driven Parsing
- Acta Electronica Sinica Vol. 41, Issue 7, Pages: 1337-1342(2013)
- 作者机构：
  
  江西财经大学信息学院数据与知识工程江西省重点实验室,江西,南昌,330013
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2013.07.015
  CLC： TP391.1
- Published：2013
- 稿件说明：
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YUAN Li-chi. Smooth Technologies in Head-Driven Parsing[J]. Acta Electronica Sinica, 2013, 41(7): 1337-1342.
DOI：

YUAN Li-chi. Smooth Technologies in Head-Driven Parsing[J]. Acta Electronica Sinica, 2013, 41(7): 1337-1342. DOI： 10.3969/j.issn.0372-2112.2013.07.015.

摘要

解决数据稀疏问题是中心词驱动句法分析中的一个重要问题

基于词类的统计语言模型是解决统计模型数据稀疏问题的重要方法.本文在分析经典平滑算法的基础上

提出一种基于语义依存信息和互信息的词聚类算法

并利用绝对权重差分方法构造了一种可变长语言模型

即根据历史词对当前词预测所作的贡献不同

值的大小也随之变化.进而提出了一种基于语义类和可变长模型的中心词驱动句法分析改进模型

既增强了句法分析模型的消歧能力

又解决了严重的数据稀疏问题.改进模型性能有了明显的提高

精确率和召回率分别为84.53%和82.41%

综合指标

值比Collins的中心词驱动句法分析模型提高了2.02个百分点.

Abstract

Solving the data sparseness problem is an important problem about head-driven parsing

cluster-based statistic language model is an important method to solve the problem of sparse data.Based on the analysis of the classical smoothing technology

this paper proposes a word clustering algorithm by utilizing mutual information and semantic dependency

and an absolute weighted difference method was presented and was used to construct vari-gram language model which has good predictable ability

then proposes an improved head-driven parsing model based on word cluster and vari-gram model.Experiments are conducted for the refined statistical parser

it achieves 84.53% precision and 82.41% recall

F measure is improved 2.02% comparing with the head-driven parsing model introduced by Collins.

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