Ontology-Based Event Modeling for Semantic Understanding ....ppt

For evaluation of our designed NOEM, we compare it with existing event models. Result shows our model has a compact structure and strong expression ability and suitable for Chinese news domain. For evaluation of 5ws extraction, please see our previous work. * For the Task 1, we propose a method of TBKEE, e.g., Title Based Key Event Extraction. The detail of our algorithm is listed here, the main idea of it is to use surface and semantic characteristics of a news story to identify a most important sentence which has the highest possibility to describe the key event of the story. Features used in this method include: Term frequency , Sentence location, Sentence length, Name entity, similarity between sentence and title. Specially, we by analyzing words co-occurrence of Title Topic words, the stress the importance of an informative title to evaluate the topic sentence. * For the second step, we design a Chinese News Semantic Elements Extraction method which comprise a serial of algorithms. For example, we use a HMM-based NER tool to recognize NE, design a CRF-based NP tagger to recognize NP, a verb-driven SVM method to identify Event, and a Syntactic-semantic rules-based algorithm to recognize event triple Subject, Predicate, Object. For sake of time, please see our previous work. * Outline Introduction Related Work Event Definitions Existing Event Models News Ontology Event Model The Design of NOEM Main Concepts and Properties in NOEM Evaluation Conclusion -*- NLPCC, Beijing, China Conclusion -*- NLPCC, Beijing, China Main contributions an extensive investigation of “event” and “event modeling” the usage of concept of 5W1H semantic elements in Chinese news domain the design of ontology-based event model: NOEM defining concepts of entities (time, person, location, organization etc.), events and relationships to capture temporal, spatial, information, experiential, structural and causal aspect, e.g. the 5W1H, of an event Future work building a news events knowl