The nature of statistical learning theory
The nature of statistical learning theory
Introduction to the special issue on temporal information processing
ACM Transactions on Asian Language Information Processing (TALIP) - Special Issue on Temporal Information Processing
The predictive power of online chatter
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
ARSA: a sentiment-aware model for predicting sales performance using blogs
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Can blog communication dynamics be correlated with stock market activity?
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Experiments on Personal Opinion Expression and Consensus Building using "Future Chronicle
ISUC '08 Proceedings of the 2008 Second International Symposium on Universal Communication
Supporting analysis of future-related information in news archives and the web
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
ChronoSeeker: Future Opinion Extraction
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Analyzing collective view of future, time-referenced events on the web
Proceedings of the 19th international conference on World wide web
Improving Retrieval of Future-Related Information in Text Collections
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Proceedings of the 21st ACM international conference on Information and knowledge management
Disambiguating Implicit Temporal Queries by Clustering Top Relevant Dates in Web Snippets
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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In this paper, we propose an on-demand search engine called ChronoSeeker, which allows users to find past/future events based on their interest. Our goal is providing a search engine which can collect as many future/past events as possible relevant to user's query in obtaining various future scenarios considering both predictions and histories. Two technical issues are treated, (1) efficient search method for event information and (2) accurate filtering method for removing noises from search results. To search for event information effectively, our system expands a user query by some typical expressions related to event information such as year expressions, temporal modifiers and context terms. To remove noisy information, we selected five types of features for a machine learning technique to classify candidates into event information or not. Our experiment showed that filtering performance achieved an 85% F-measure, and that query expansion can collect dozens of times more CEs than those without expansion.