基于HowNet和PMI的词语情感极性计算

摘要

基于语料库的点互信息(PMI)计算方法依赖于语料库的完善性,基于HowNet的计算方法则依赖于知网相似度计算的准确性.为克服2种方法的局限性,提出一种HowNet和PMI相融合的词语极性计算方法,利用知网进行同义词扩展,降低情感词在语料库中出现频率低所带来的问题.实验结果表明,该方法的微平均和宏平均性能比传统方法提升约5％.%This paper describes the face recognition algorithm of Histograms of Oriented Gradients(HOG), and designs face recognition experiment based on HOG The Experiment is done on the Face Recognition Technology(FERET) face database. It testes the effect of different HOG parameters on face recognition and tries to find the optimal parameter settings. Experimental results show that the choice of space and range in gradient direction of HOG feature is the same on pedestrian detection and face recognition. The different block mode has different effects too. HOG descriptor can express face effectively when it produce less characteristic dimension in non-overlapping manner. Recognition performance improves significantly when it is standardized.