Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Mining models of human activities from the web
Proceedings of the 13th international conference on World Wide Web
ACM SIGKDD Explorations Newsletter
Learning domain ontologies for Web service descriptions: an experiment in bioinformatics
WWW '05 Proceedings of the 14th international conference on World Wide Web
Common sense data acquisition for indoor mobile robots
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Learning large scale common sense models of everyday life
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Structure learning on large scale common sense statistical models of human state
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Domain Ontology Learning from the Web
Domain Ontology Learning from the Web
Task knowledge based retrieval for service relevant to mobile user's activity
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Construction and use of role-ontology for task-based service navigation system
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Automatically constructing concept hierarchies of health-related human goals
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Intuitive Topic Discovery by Incorporating Word-Pair's Connection Into LDA
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
SpinRadar: a spontaneous service provision middleware for place-aware social interactions
Personal and Ubiquitous Computing
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We have been developing a task-based service navigation system that offers to the user services relevant to the task the user wants to perform. The system allows the user to concretize his/her request in the task-model developed by human-experts. In this study, to reduce the cost of collecting a wide variety of activities, we investigate the automatic modeling of users' real world activities from the web. To extract the widest possible variety of activities with high precision and recall, we investigate the appropriate number of contents and resources to extract. Our results show that we do not need to examine the entire web, which is too time consuming; a limited number of search results (e.g. 900 from among 21,000,000 search results) from blog contents are needed. In addition, to estimate the hierarchical relationships present in the activity model with the lowest possible error rate, we propose a method that divides the representation of activities into a noun part and a verb part, and calculates the mutual information between them. The result shows almost 80% of the hierarchical relationships can be captured by the proposed method.