Performance Evaluation of English Part-of-Speech Tagging Based on Typical Parameter Smoothing Algorithm

Abstract

Part-of-speech (POS) tagging for English is the basis for implementing English automatic correction. Although researchers have done a lot of useful studies on English POS tagging, most of them are aimed at users with English as the first language, while studies for users with English as the second language are few. For this purpose, manual tagging is performed based on the typical parameter smoothing algorithm. On this basis, a performance evaluation method for English part-of-speech tagging is proposed, which integrates the features of term clustering, non-tagged corpus statistics, word pronunciation, etc. The experimental results show that the algorithm can improve the performance of POS tagging effectively, with the tagging accuracy improved from 94.49% to 97.07%