关键词: 句间关系, LIBSVM, 机器学习, kappa值, 依存词汇

Abstract:

Aiming at the low accuracy caused by the phenomenon of rule weight weakening from iterations of machine learning when judging the sentence relationships by applying rules and machine learning methods, the method of strengthening the imported obvious rule characteristics in the process of combining rules and machine learning was proposed. Firstly, these specific characteristics that having obvious rules such as dependency vocabulary, syntax and semantics information were extracted; secondly, universal characteristics were extracted based on these words that could indicate relationships; then, the characteristics were written into the data vector that to be input, and another dimensional vector was added to store the obvious rule characteristics; Finally, rules and machine learning methods were combined with LIBSVM model to perform the experiment. The experimental results show that the accuracy rate is averagely 2% higher than that before strengthening the characteristics, and all kinds of relationships' accurate rate, recall rate and F1 value show good results as a whole, their average values achieved 82.02%, 88.95% and 84.76%. The experimental ideas and methods are important for studying the compactness of adjacent sentences.

Key words: relationship between sentences, LIBSVM, machine learning, kappa value, dependency vocabulary