Text analysis is a popular technique for finding the most significant information from texts including semantic,emotional,and other hidden features,which became a research hotspot in the last few years.Specially,there...Text analysis is a popular technique for finding the most significant information from texts including semantic,emotional,and other hidden features,which became a research hotspot in the last few years.Specially,there are some text analysis tasks with judgment reports,such as analyzing the criminal process and predicting prison terms.Traditional researches on text analysis are generally based on special feature selection and ontology model generation or require legal experts to provide external knowledge.All these methods require a lot of time and labor costs.Therefore,in this paper,we use textual data such as judgment reports creatively to perform prison term prediction without external legal knowledge.We propose a framework that combines value-based rules and a fuzzy text to predict the target prison term.The procedure in our framework includes information extraction,term fuzzification,and document vector regression.We carry out experiments with real-world judgment reports and compare our model’s performance with those of ten traditional classification and regression models and two deep learning models.The results show that our model achieves competitive results compared with other models as evaluated by the RMSE and R-squared metrics.Finally,we implement a prototype system with a user-friendly GUI that can be used to predict prison terms according to the legal text inputted by the user.展开更多
A concept-based approach is expected to resolve the word sense ambiguities in information retrieval and apply the semantic importance of the concepts, instead of the term frequency, to representing the contents of a d...A concept-based approach is expected to resolve the word sense ambiguities in information retrieval and apply the semantic importance of the concepts, instead of the term frequency, to representing the contents of a document. Consequently, a formalized document framework is proposed. The document framework is used to express the meaning of a document with the concepts which are expressed by high semantic importance. The framework consists of two parts: the "domain" information and the "situation & background" information of a document. A document-extracting algorithm and a two-stage smoothing method are also proposed. The quantification of the similarity between the query and the document framework depends on the smoothing method. The experiments on the TREC6 collection demonstrate the feasibility and effectiveness of the proposed approach in information retrieval tasks. The average recall level precision of the model using the proposed approach is about 10% higher than that of traditional ones.展开更多
A personalized emotion space is proposed to bridge the "affective gap" in video affective content understanding. In order to unify the discrete and dimensional emotion model, fuzzy C-mean (FCM) clustering algorith...A personalized emotion space is proposed to bridge the "affective gap" in video affective content understanding. In order to unify the discrete and dimensional emotion model, fuzzy C-mean (FCM) clustering algorithm is adopted to divide the emotion space. Gaussian mixture model (GMM) is used to determine the membership functions of typical affective subspaces. At every step of modeling the space, the inputs rely completely on the affective experiences recorded by the audiences. The advantages of the improved V-A (Velance-Arousal) emotion model are the per- sonalization, the ability to define typical affective state areas in the V-A emotion space, and the convenience to explicitly express the intensity of each affective state. The experimental results validate the model and show it can be used as a personalized emotion space for video affective content representation.展开更多
基金support of the Science&Technology Development Project of Hangzhou Province,China(Grant No.20162013A08)the Research Project Support for Education of Zhejiang Province,China(Grant No.Y201941372)。
文摘Text analysis is a popular technique for finding the most significant information from texts including semantic,emotional,and other hidden features,which became a research hotspot in the last few years.Specially,there are some text analysis tasks with judgment reports,such as analyzing the criminal process and predicting prison terms.Traditional researches on text analysis are generally based on special feature selection and ontology model generation or require legal experts to provide external knowledge.All these methods require a lot of time and labor costs.Therefore,in this paper,we use textual data such as judgment reports creatively to perform prison term prediction without external legal knowledge.We propose a framework that combines value-based rules and a fuzzy text to predict the target prison term.The procedure in our framework includes information extraction,term fuzzification,and document vector regression.We carry out experiments with real-world judgment reports and compare our model’s performance with those of ten traditional classification and regression models and two deep learning models.The results show that our model achieves competitive results compared with other models as evaluated by the RMSE and R-squared metrics.Finally,we implement a prototype system with a user-friendly GUI that can be used to predict prison terms according to the legal text inputted by the user.
基金The National Basic Research Program of China(973Program)(No.2004CB318104),the Knowledge Innovation Pro-gram of Chinese Academy of Sciences (No.13CX04).
文摘A concept-based approach is expected to resolve the word sense ambiguities in information retrieval and apply the semantic importance of the concepts, instead of the term frequency, to representing the contents of a document. Consequently, a formalized document framework is proposed. The document framework is used to express the meaning of a document with the concepts which are expressed by high semantic importance. The framework consists of two parts: the "domain" information and the "situation & background" information of a document. A document-extracting algorithm and a two-stage smoothing method are also proposed. The quantification of the similarity between the query and the document framework depends on the smoothing method. The experiments on the TREC6 collection demonstrate the feasibility and effectiveness of the proposed approach in information retrieval tasks. The average recall level precision of the model using the proposed approach is about 10% higher than that of traditional ones.
基金Supported by the National Natural Science Foundation of China (60703049)the "Chenguang" Foundation for Young Scientists (200850731353)the National Postdoctoral Foundation of China (20060400847)
文摘A personalized emotion space is proposed to bridge the "affective gap" in video affective content understanding. In order to unify the discrete and dimensional emotion model, fuzzy C-mean (FCM) clustering algorithm is adopted to divide the emotion space. Gaussian mixture model (GMM) is used to determine the membership functions of typical affective subspaces. At every step of modeling the space, the inputs rely completely on the affective experiences recorded by the audiences. The advantages of the improved V-A (Velance-Arousal) emotion model are the per- sonalization, the ability to define typical affective state areas in the V-A emotion space, and the convenience to explicitly express the intensity of each affective state. The experimental results validate the model and show it can be used as a personalized emotion space for video affective content representation.