In order to recognize one＇s intention from the communication, both the meaning and the emotion are necessary to be interpreted correctly. But until now the study of fine-grained theory of emotion is still full of cha...In order to recognize one＇s intention from the communication, both the meaning and the emotion are necessary to be interpreted correctly. But until now the study of fine-grained theory of emotion is still full of challenges. This paper analy- zes emotion category according to the statistics of Affective Word （AW） hierarchy and descries an e- motion ontology from Chinese knowledge resource semi-automatically created for human machine in- teraction. The emotion hierarchy is called complex emotion. Firstly, over 7 000 AWs have been annota- ted and theft detailed explanations had been collected for an affective lexicon and then the consistent rela- tionships are automatically parsed and a serial of e- motion hierarchical structures are built up. More than 50 affective categories are extracted by a lexical clustering algorithm and about 5 000 nouns and ad- jectives and 2 000 verbs are categorized into the predicate hierarchy. The results have been evaluated to be valid by two metrics.展开更多
基金supported by the Ministry of Education,Science,Sports and Culture,Grant-in-Aid for Scientific Research under Grant No.22240021the Grant-in-Aid for Challenging Exploratory Research under Grant No.21650030
文摘In order to recognize one＇s intention from the communication, both the meaning and the emotion are necessary to be interpreted correctly. But until now the study of fine-grained theory of emotion is still full of challenges. This paper analy- zes emotion category according to the statistics of Affective Word （AW） hierarchy and descries an e- motion ontology from Chinese knowledge resource semi-automatically created for human machine in- teraction. The emotion hierarchy is called complex emotion. Firstly, over 7 000 AWs have been annota- ted and theft detailed explanations had been collected for an affective lexicon and then the consistent rela- tionships are automatically parsed and a serial of e- motion hierarchical structures are built up. More than 50 affective categories are extracted by a lexical clustering algorithm and about 5 000 nouns and ad- jectives and 2 000 verbs are categorized into the predicate hierarchy. The results have been evaluated to be valid by two metrics.
