Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
PaperLink: a technique for hyperlinking from real paper to electronic content
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Insight lab: an immersive team environment linking paper, displays, and data
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
WebStickers: using physical tokens to access, manage and share bookmarks to the Web
DARE '00 Proceedings of DARE 2000 on Designing augmented reality environments
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
| Hi-index | 0.00 |
In today's world, the digital information retrieval experience is inherently a sparse device-centric activity. Users rely on the ability of the currently used device to supply the requested information, in some disconnection from past activities on other devices. There is a growing need to develop new methods of connecting cross-context information retrieval sessions. We present PalimPost, a converged system for storing, searching, and sharing digital and physical world information using sticky notes and mobile devices. PalimPost extracts contextual cues from a user's physical environment and activities, and connects them to the user's digital world research. Subsequently, the system presents systematically categorized information that is relevant to the moment of interaction in a just-in-time manner. PalimPost uses physical sticky notes with embedded QR codes, as well as virtual sticky notes on mobile devices. The system incorporates Automatic Speech Recognition (ASR), Optical Character Recognition (OCR), and Natural Language Processing (NLP) techniques for understanding and categorizing the content.