Automated Semantc Relation Annotation for Italian and English
msra(2010)
摘要
This paper addresses the problem of the denition of a semantic rela- tion set which can be hopefully applied to more than one language. Here I propose a very simple framework with three general semantic relation classes: association, taxonomy and space. In order to train a classier for those classes in italian and english I run an experiment with Part- Of-Speech, End-of-Sentence and Named Entity features from TextPro, a text tool suite for italian and english, and I compared three machine learning algorithms (decision lists, decision trees and support vectors) to retrieve if-then rules that worked best on general-purpose datasets (BNC for english and CORIS for italian). From the rules I developed a software, called oRA, that is compatible with TextPro itself, it obtained an aver- age F-measure of 0.789 for english and 0.781 for italian with decision lists rules.
更多查看译文
关键词
part of speech,decision tree,support vector,machine learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络