基于特征模板的条件随机场快速并行计算技术

Feature Template-Based Parallel Computation Technique for Conditional Random Fields

摘要: 条件随机场(CRFs)是现今较为流行的一种概率图模型，已经被广泛地应用到自然语言处理、生物计算、计算机视觉等领域当中。在实际应用中，采用基于上下文特征的特征函数创建法往往会产生大规模的特征函数，造成因计算复杂度过大而建模困难，模型无法高效率地训练生成。本文提出一种基于特征模板的快速并行计算技术，通过观察特征模板创建的上下文特征函数主要特点，对M矩阵进行并行处理，降低计算量。实验结果表明，本文提出的快速计算方法较传统方法在速度上有非常大的优势。

Abstract: Conditional Random Fields (CRFs) is a popular probabilistic graphical model, which has been applied in a wide range of areas, including Natural Language Process (NLP), Bioinformatics, Computer Vision, etc. However, con- textual features-based methods usually lead to large-scale feature functions and result in high computational complexity and low model training efficiency. In this paper, a feature template-based parallel computation technique is proposed to parallelly process M matrix and reduce computational complexity through observing the main feature of contextual feature function created by the template. Experimental results show that our approach significantly outperforms tradi- tional feature function approach on computation speed.