$t3$ have both fired, resulting in a token in both $p4$ and $p5$, $t4$ is enabled. Executing $t4$ takes the token from both $p4$ and $p5$, and puts a token in $p6$. The **f** indicates that the process execution can stop in the marking consisting of this place. Alternatively, it can fire $t5$, taking the token from $p6$ and placing a token in $p2$ and $p5$, which allows for execution of $MC$ and $W$ to reach the marking consisting of $p6$ again. We refer the interested reader to [@Reisig2012] for an extensive review of Petri nets.

Conditional Random Field {#sec:crf}
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We view the recognition of high-level event labels as a sequence labeling task in which each event is classified as one of the higher-level events from a high-level event alphabet. Conditional Random Fields (CRFs) [@Lafferty2001] are a type of probabilistic graphical model which has become popular in the fields of language processing and computer vision for the task of sequence labeling. A Conditional Random Field models the conditional probability distribution of the label sequence given an observation sequence using a log-linear model. We use Linear-chain Conditional Random Fields, a subclass of Conditional Random Fields that has been widely used 