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A symbolic approach to automatic multiword term structuring   总被引:1,自引:0,他引:1  
This paper presents a three-level structuring of multiword terms basing on lexical inclusion, WordNet similarity and a clustering approach. Term clustering by automatic data analysis methods offers an interesting way of organizing a domain’s knowledge structure, useful for several information-oriented tasks like science and technology watch, textmining, computer-assisted ontology population, Question Answering (Q–A). This paper explores how this three-level term structuring brings to light the knowledge structures from a corpus of genomics and compares the mapping of the domain topics against a hand-built ontology (the GENIA ontology). Ways of integrating the results into a Q–A system are discussed.  相似文献   
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Persuasive technologies (PTs) motivate behaviour change using various persuasive strategies. However, there is still a dearth of knowledge on how PTs motivate behaviour change and how to design systems to increase their persuasiveness. To provide empirical insight into the mechanism through which PTs persuade, we conducted a large-scale study with 543 participants to investigate the relation between Attention, Relevance, Confidence, and Satisfaction constructs from the ARCS model of motivation and 10 strategies that are commonly used in persuasive systems design. Our results show that the ARCS constructs collectively explain between 82% and 91% of the variance in persuasiveness across the ten strategies. Relevance, followed by Attention, has the strongest association with persuasiveness. The result generalises across gender groups. Therefore, to increase a system’s persuasiveness, designers should focus on designing to increase relevance and to capture user’s attention, while also promoting confidence and a feeling of satisfaction. We contribute to Human–Computer Interaction (HCI) and Persuasive Technology by offering design guidelines for PTs to increase their persuasiveness and hence efficacy.  相似文献   
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