In this paper,we provide a Word Emotion Topic(WET) model to predict the complex word emotion information from text,and discover the distribution of emotions among different topics.A complex emotion is defined as the c...In this paper,we provide a Word Emotion Topic(WET) model to predict the complex word emotion information from text,and discover the distribution of emotions among different topics.A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories:joy,love,expectation,surprise,anxiety,sorrow,anger and hate.We use a hierarchical Bayesian network to model the emotions and topics in the text.Both the complex emotions and topics are drawn from raw texts,without considering any complicated language features.Our experiment shows promising results of word emotion prediction,which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields(CRFs) on raw text.We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.展开更多
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 ch...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 analyzes emotion category according to the statistics of Affective Word(AW) hierarchy and describes an emotion ontology from Chinese knowledge resource semi-automatically created for human machine interaction.The emotion hierarchy is called complex emotion.Firstly,over 7 000 AWs have been annotated and their detailed explanations had been collected for an affective lexicon and then the consistent relationships are automatically parsed and a serial of emotion hierarchical structures are built up.More than 50 affective categories are extracted by a lexical clustering algorithm and about 5 000 nouns and adjectives 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
Two new taxane diterpenoids from Taxus yunnanensis were elucidated by spectroscopic techniques, one of which was identified as a unique taxane with 2,20-oxo group.
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 this paper,we provide a Word Emotion Topic(WET) model to predict the complex word emotion information from text,and discover the distribution of emotions among different topics.A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories:joy,love,expectation,surprise,anxiety,sorrow,anger and hate.We use a hierarchical Bayesian network to model the emotions and topics in the text.Both the complex emotions and topics are drawn from raw texts,without considering any complicated language features.Our experiment shows promising results of word emotion prediction,which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields(CRFs) on raw text.We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.
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 analyzes emotion category according to the statistics of Affective Word(AW) hierarchy and describes an emotion ontology from Chinese knowledge resource semi-automatically created for human machine interaction.The emotion hierarchy is called complex emotion.Firstly,over 7 000 AWs have been annotated and their detailed explanations had been collected for an affective lexicon and then the consistent relationships are automatically parsed and a serial of emotion hierarchical structures are built up.More than 50 affective categories are extracted by a lexical clustering algorithm and about 5 000 nouns and adjectives and 2 000 verbs are categorized into the predicate hierarchy.The results have been evaluated to be valid by two metrics.
文摘Two new taxane diterpenoids from Taxus yunnanensis were elucidated by spectroscopic techniques, one of which was identified as a unique taxane with 2,20-oxo group.
