A Classification Approach to Identify Definitions in Aviation Domain
Xu Pan, Hongbin Gu, Chanjuan Sun · 2009
In this paper, we introduce a classification approach to identify definitions of all terms from a aviation professional corpus. The corpora of aviation domain are firstly segmented by LTP platform from HIT. Then four feature selection methods and two classifiers are applied to extract definitions. First of all, we summarize the correct proportion of feature subset used in classification of term definitions, and secondly argue that the naive Bayes classifier combined with CHI or ODDS for feature selection achieve the best score in the Fl-measure and F2-measure. In the end, we recognize that the use of SVM classifier with linear kernel could achieve very high precision, but the worst recall.