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To generate natural and less hardwired facial expression for humanoid robots, a facial motion imitation method, which transfers facial geometric characteristics of humans to robots, is proposed. Firstly, for mapping servo control space into facial expression space, a forward kinematics model with sequence is built based on recurrent neural network. Secondly, the process of expression imitation for...
Along with the development of social network, more and more people know the world by reading news. The problem about what kind of emotion is inspired when people read news is very worthy of discussion. This paper will mix Deep Belief Networks (DBN) model and Support Vector Machine (SVM) to a hybrid neural network model by using the Contrast Divergence (CD) algorithm to estimate the weights when training...
This paper presents a novel WLAN-based indoor localization algorithm (i.e., HED) to combat the environmental dynamics by tolerating the sequence disorders caused by AP (access point) changes, while harvesting from the bursting number of available wireless resources. Via extensive real-world experiments lasting for over 6 months, we show the superiority of our HED algorithm in terms of accuracy and...
Indoor localization remains a hot topic and receives tremendous research efforts during the last few decades. While most previous efforts focus on the designing issue, little effort has been paid to the impact of different environmental parameters on the system performance. To this end, we present an extensive empirical study with real-world experiments to provide sufficient data for analysis. By...
With the development of artificial intelligence and pattern recognition, facial expression recognition plays a more and more important role in intelligent human-computer interaction. In this paper, we present a model named K-order emotional intensity model (K-EIM) which is based on K-Means clustering. Different from other related works, the proposed approach can quantify emotional intensity in an...
This paper presents a DBN (deep belief nets) model and a multi-modality feature extraction method to extend features' dimensionalities of short text for Chinese micro blogging sentiment classification. Besides traditional features sets for document classification, comments for certain posts are also extracted as part of the micro blogging features according to the relationship between commenters and...
With the growth of the Internet and electronic commerce, there is more and more review data on the Internet. Quite a lot of Internet users refer to related comments of a product before they make a decision, which can teach them about the quality and reputation of the product and help them decide whether to buy it. A system that can automatically classify the polarity of a given text would be a great...
In this paper, Deep Belief Nets(DBN) model and Support Vector Machine(SVM) are used to mine the features hidden in social news, which can influence the emotions of men. Three feature selection methods for text modeling are used to build the input vectors of DBN, with the purpose of keeping the text information to the greatest extent. We take advantage of the deep features abstracted by DBN to build...
The paper studies a new method for Chinese new word detection and emotional tendency judgment based on mixed model and proposes a new word generation framework. First we construct conditional random fields (CRFs) to recognize the new words, lead-in features based on character combined with the crowd sourcing network dictionary. And then express word as a word vector based on neural network language...
With plenty of online resources constantly increasing (like weblog, product reviews, news reviews, etc.), it is difficult to read them and obtain the useful information, especially emotion information. The emotion analysis on internet online information has received much attention from natural language processing field in recent years. In most existing works, single-label emotion analysis have been...
Text emotion analysis suffers from the lack of faithful emotion features, and the difficulty of mining multiple emotions that are mixed together. In this paper, we provide a Gibbs sampling method to solve these two problems. We explicitly characterize the emotion combination phenomenons, and predict the complex emotions of words together with the emotion intensities for each singular emotion through...
Keywords are the critical resources of information management and retrieval, automatic text classification and clustering. The keywords extraction plays an important role in the process of constructing structured text. Current algorithms of keywords extraction have matured in some ways. However the errors of word segmentation which caused by unknown words have been affected the performance of Chinese...
Sentence similarity computing plays an important role in the nature language processing. Many different methods are proposed to calculate sentence similarity including word, semantic, syntax and so on. In this paper, we proposed a sentence similarity method for travel question answering system by combining the word context information and semantic similarity together. We searched a series of context...
This paper presents an integrated method on question classification of Chinese Sichuan cuisine QA system. Classified features are extracted by means of domain attributes and the rule based classifier is constructed. SVM classifier is used for secondary classification to the questions which cannot be matched with rules. Experimental results show that the proposed method can achieve an accuracy of 96...
In Chinese language processing, new words are particularly problematic. It is impossible to get a complete dictionary as new words can always be created. We proposed a unified dual-chain unequal-state CRF model to detect new words together with their part-of-speech in Chinese texts regardless of the word types such as compound words, abbreviation, person names, etc. The dual-chain unequal-state CRF...
This paper proposes an novel approach to annotate function tags for unparsed text. What distinguishes our work from other attempts in such task is that we assign function tags directly basing on lexical information other than on parsed trees. In order to demonstrate the effectiveness and versatility of our method, we investigate two statistical models for automatic annotation, one is log-linear maximum...
Recent years have seen great process in studying English question classification. In our research, we learn Chinese question classification by exploiting the result of lexical, syntactic and semantic parsing on question sentences. Support vector machines are adopted to train a classifier on 6 coarse categories using single and combination of different parsing results as features. We find that even...
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