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Extracting temporal information from raw text is fundamental for deep language understanding, and key to many applications like question answering, information extraction, and document summarization. Our long-term goal is to build complete temporal structure of documents and apply the temporal structure in other applications like textual entailment, question answering, dialog systems or others. In this paper, we present a first step, a system for extracting event, event features, temporal expression and its normalized values from raw text. Our system is a combination of deep semantic parsing with extraction rules, Markov Logic Network classifiers and Conditional Random Field classifiers. To compare with existing systems, we evaluated our system on the TimeBank corpus. Our system outperforms or does equally well with all existing systems that evaluate on the TimeBank corpus and our performance is very close to inter-annotator agreement of the TimeBank annotators.