A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Viewing morphology as an inference process
Artificial Intelligence - Special issue on Intelligent internet systems
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Modeling information incorporation in markets, with application to detecting and explaining events
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Proceedings of the 3rd international conference on Knowledge capture
LoLo: a system based on terminology for multilingual extraction
IEBeyondDoc '06 Proceedings of the Workshop on Information Extraction Beyond The Document
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Metadata information plays a crucial role in augmenting document organising efficiency and archivability. News metadata includes DateLine, ByLine, HeadLine and many others. We found that HeadLine information is useful for guessing the theme of the news article. Particularly for financial news articles, we found that HeadLine can thus be specially helpful to locate explanatory sentences for any major events such as significant changes in stock prices. In this paper we explore a support vector based learning approach to automatically extract the HeadLine metadata. We find that the classification accuracy of finding the HeadLines improves if DateLines are identified first. We then used the extracted HeadLines to initiate a pattern matching of keywords to find the sentences responsible for story theme. Using this theme and a simple language model it is possible to locate any explanatory sentences for any significant price change.