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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. In this paper, we describe two systems we submitted to the TempEval 2 challenge, for extracting temporal information from raw text. The systems use a combination of deep semantic parsing, Markov Logic Networks and Conditional Random Field classifiers. Our two submitted systems, TRIPS and TRIOS,dblp:conf/semeval/UzZamanA10 fatcat:htvthgwxd5bzfprxaevkk7fisu