Information extraction from HTML: application of a general machine learning approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Forecasting Change Directions for Financial Time Series Using Hidden Markov Model
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
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Electronic information grows rapidly as the Internet is widely used in our daily life. In order to identify the exact information for the user query, information extraction is widely researched and investigated. The template, which pertains to events or situations, and contains slots that denote who did what to whom, when, and where, is predefined by a template builder. Therefore, fixed templates are the main obstacles for the information extraction system out of the laboratory. In this paper, a method to automatically discover the event pattern in Chinese from stock market bulletin is introduced. It is based on the tagged corpus and the domain model. The pattern discovery process is independent of the domain model by introducing a link table. The table is the connection between text surface structure and semantic deep structure represented by a domain model. The method can be easily adapted to other domains by changing the link table.